Hi Francesc and All, On 31 October 2012 21:02, Francesc Alted wrote: > On 10/31/12 10:12 AM, Andrea Gavana wrote: >> Hi Francesc & All, >> >> On 31 October 2012 14:13, Francesc Alted wrote: >>> On 10/31/12 4:30 AM, Andrea Gavana wrote: >>>> Thank you for all your suggestions. I managed to slightly modify the >>>> script you attached and I am also experimenting with compression. >>>> However, in the newly attached script the underlying table is not >>>> modified, i.e., this assignment: >>>> >>>> for p in table: >>>> p['results'][:NUM_SIM, :, :] = numpy.random.random(size=(NUM_SIM, >>>> len(ALL_DATES), 7)) >>>> table.flush() >>> For modifying row values you need to assign a complete row object. >>> Something like: >>> >>> for i in range(len(table)): >>> myrow = table[i] >>> myrow['results'][:NUM_SIM, :, :] = >>> numpy.random.random(size=(NUM_SIM, len(ALL_DATES), 7)) >>> table[i] = myrow >>> >>> You may also use Table.modifyColumn() for better efficiency. Look at >>> the different modification methods here: >>> >>> http://pytables.github.com/usersguide/libref/structured_storage.html#table-methods-writing >>> >>> and experiment with them. >> Thank you, I have tried different approaches and they all seem to run >> more or less at the same speed (see below). I had to slightly modify >> your code from: >> >> table[i] = myrow >> >> to >> >> table[i] = [myrow] >> >> To avoid exceptions. >> >> In the newly attached file, I switched to blosc for compression (but >> with compression level 1) and run a few sensitivities. By calling the >> attached script as: >> >> python pytables_test.py NUM_SIM >> >> where "NUM_SIM" is an integer, I get the following timings and file sizes: >> >> C:\MyProjects\Phaser\tests>python pytables_test.py 10 >> Number of simulations : 10 >> H5 file creation time : 0.879s >> Saving results for table: 6.413s >> H5 file size (MB) : 193 >> >> >> C:\MyProjects\Phaser\tests>python pytables_test.py 100 >> Number of simulations : 100 >> H5 file creation time : 4.155s >> Saving results for table: 86.326s >> H5 file size (MB) : 1935 >> >> >> I dont think I will try the 1,000 simulations case :-) . I believe I >> still don't understand what the best strategy would be for my problem. >> I basically need to save all the simulation results for all the 1,200 >> "objects", each of which has a timeseries matrix of 600x7 size. In the >> GUI I have, these 1,200 "objects" are grouped into multiple >> categories, and multiple categories can reference the same "object", >> i.e.: >> >> Category_1: object_1, object_23, object_543, etc... >> Category_2: object_23, object_100, object_543, etc... >> >> So my idea was to save all the "objects" results to disk and, upon the >> user's choice, build the categories results "on the fly", i.e. by >> seeking the H5 file on disk for the "objects" belonging to that >> specific category and summing up all their results (over time, i.e. >> the 600 time-steps). Maybe I would be better off with a 4D array >> (NUM_OBJECTS, NUM_SIM, TSTEPS, 7) as a table, but then I will lose the >> ability to reference the "objects" by their names... > > You should keep trying experimenting with different approaches and > discover the one that works for you the best. Regarding using the 4D > array as a table, I might be misunderstanding your problem, but you can > still reference objects by name by using: > > row = table.where("name == %s" % my_name) > table[row.nrow] = ... > > You may want to index the 'name' column for better performance.
I did spend quite some time experimenting today (actually almost the whole day), but even the task of writing a 4D array (created via createCArray) to disk is somehow overwhelming from a GUI point of view. My 4D array is a 1,200x100x600x7, and on my PC at work (16 cores, 96 GB RAM, 3.0 GHz Windows Vista 64bit with PyTables pro) it takes close to 80 seconds to populate it with random arrays and save it to disk. This is almost a lifetime in the GUI world, and 100 simulation is possibly the simplest case I have. As I said before, I am probably completely missing the point; the fact that my script seems to be "un-improvable" in terms of speed is quite a demonstration of it. But given the constraints (in terms of time and GUI responsiveness) we have I will probably go back to my previous approach of saving the simulation results for the higher level categories only, discarding the ones for the 1,200 "objects". I love the idea of being able to seek results in real-time via a HDF5 file on my drive and having all the simulation results readily available, but the actual "saving" time is somehow a showstopper. Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://www.infinity77.net # ------------------------------------------------------------- # def ask_mailing_list_support(email): if mention_platform_and_version() and include_sample_app(): send_message(email) else: install_malware() erase_hard_drives() # ------------------------------------------------------------- # ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
