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On Wed, Mar 11, 2015 at 6:11 PM, Dumindu Jayasekera < d.jayasekera@xxxxxxxxxxxxxxxxx> wrote: > Each time step is a different file. Original final name of the > "TEST_file.csv is TRMM_1998_01_0100_newntcl.csv. So the next file for next > time step > is TRMM_1998_01_0103_newntcl.csv, TRMM_1998_01_0106_newntcl.csv, > TRMM_1998_01_0109_newntcl.csv > and so on. I have the time steps until TRMM_1998_02_1512_newntcl.csv. > > Do you know how to loop through file names and store in arrays? > This is pretty basic stuff in any scripting language -- you're going to need to learn a bit of Python, or something, so get this done -- I encourage you to take the time to do so -- it will serve you well. This: http://learnpythonthehardway.org/ is a pretty good crash course in basic python. Then you'll want to know a bit about numpy. Google "intro to numpy" or "numpy tutorial" -- you'll find many options. -Chris > Thanks again. > > > > > On Wed, Mar 11, 2015 at 3:19 PM, Chris Barker <chris.barker@xxxxxxxx> > wrote: > >> Sorry, I really dont have time to do it for you, but a couple hints: >> >> IF you're lucky, you can "reshape" the input arrays in one step: >> >> data = np.loadtxt('TEST_file.csv', delimiter=',') >> >> lat = data[:,0] # the first column -- if that is latitude >> >> lat = lat.reshape( (num_times, num_lats, num_lons) ) >> >> That _might put it all in the right order, depending on hoe it's written >> to the file. If that doesn't work, this might: >> >> lat = lat.reshape( (num_times, num_lats, num_lons), order='F') >> >> If that doesn't work, then you may have to loop through all of by hand, >> in the right order: >> >> new_data = np.zeros( (num_times, num_lats, num_lons) ) >> i = 0 >> for t in range(num_times): >> for lat in range(num_lats): >> for lon in range(num_lons): >> new_data[t, lat, lon] = old_data[i] >> i += 1 >> >> You may need to change the order of those loops to match your data. >> >> Also,m I see three columns, not four in your sample file -- is each time >> step in a different file? That wold require you to loop through all the >> files to load each time... >> >> Take a look at python and numpy tutorials online to learn a bit more >> about all this. >> >> -Chris >> >> >> >> >> On Wed, Mar 11, 2015 at 12:03 PM, Dumindu Jayasekera < >> d.jayasekera@xxxxxxxxxxxxxxxxx> wrote: >> >>> Chris, >>> >>> Thanks again. I am unable to create the 3D array since I have limited >>> knowledge in python. >>> >>> I have attached the csv files, python scripts and sample netCDF file. >>> >>> Any help is appreciated. >>> >>> >>> >>> >>> >>> On Wed, Mar 11, 2015 at 8:45 AM, Chris Barker <chris.barker@xxxxxxxx> >>> wrote: >>> >>>> On Tue, Mar 10, 2015 at 5:50 PM, Dumindu Jayasekera < >>>> d.jayasekera@xxxxxxxxxxxxxxxxx> wrote: >>>> >>>>> Thanks again Chris. >>>>> >>>>> I think I dont need to core metadata. I was able to produce the .nc >>>>> file using the code below (as you suggested). >>>>> >>>>> >>>> As Rich suggests, you may be better off using onf the libraries >>>> suggested, Iris, in particular will get all the complex CF metadaat stuff >>>> right for you. >>>> >>>> But you're this close, so... >>>> >>>> >>>>> But, how can I modify to rename the var1 = lat, var2 = lon, var3 = >>>>> precipitation in the code below.? >>>>> >>>> >>>> From the nc file you sent, it looks like you want the precipitiaon to >>>> be a 3-d array: (time X ltitude X longitude), so: >>>> >>>> >>>> # this load the file into a Nx3 array (three columns) >>>>> data = np.loadtxt('TEST_file.csv', delimiter=',') >>>>> >>>> >>>> note -- you dont have a 3-d array here, you have a 2-d array, which is >>>> really three vectors -- you will need to re-shuffle these to get the 3-d >>>> array you want, and the time, lat, and long vectors.... but assumign you've >>>> done that: >>>> >>>> >>>> you'll need three dimensions: >>>> time = ds.createDimension('time', num_times) >>>> lat = ds.createDimension('latitude', num_latitude) >>>> lon = ds.createDimension('longitude', num_longitude) >>>> >>>> Then you want your 3-d precip variable -- I"d call it something more >>>> readable than "r", but maybe you need that... >>>> >>>> r = ds.createVariable('r', np.float32, ('time', 'latitude', >>>> 'longitude')) >>>> var[:] = data[:,:,:] >>>> ## adds some attributes >>>> var.units = 'mm' >>>> var.long_name = 'Precipitation' >>>> ... and the others that you need. >>>> >>>> HTH, >>>> >>>> -Chris >>>> >>>> -- >>>> >>>> Christopher Barker, Ph.D. >>>> Oceanographer >>>> >>>> Emergency Response Division >>>> NOAA/NOS/OR&R (206) 526-6959 voice >>>> 7600 Sand Point Way NE (206) 526-6329 fax >>>> Seattle, WA 98115 (206) 526-6317 main reception >>>> >>>> Chris.Barker@xxxxxxxx >>>> >>> >>> >> >> >> -- >> >> Christopher Barker, Ph.D. >> Oceanographer >> >> Emergency Response Division >> NOAA/NOS/OR&R (206) 526-6959 voice >> 7600 Sand Point Way NE (206) 526-6329 fax >> Seattle, WA 98115 (206) 526-6317 main reception >> >> Chris.Barker@xxxxxxxx >> > > -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@xxxxxxxx
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