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
