Contents	Previous	Next

Goal: To save the files global attributes together with the variable attribute to use them when writing a derived variable to a new file.

You can download and run the python script redecorate.py or follow the tutorial and type commands at the cdat or python prompt.

Let's import the modules we will need

import cdms, vcs, cdutil, genutil, cdtime, MA, MV, sys, os

and open a dataset we will work with

  file = os.path.join(sys.prefix,'sample_data/tas_ccsr-95a.xml')
  a=cdms.open(file)

let's import some data

data=a('tas')
print data.shape

the output is:
(12, 1, 32, 64)

Now lets get the files global attributes and put them into a dictionary called: file_dic

#
# rip out global attributes and put them in a dictionary called: file_dic
#
list_file=a.attributes.keys()
file_dic={}
for i in range(0,len(list_file)):
 file_dic[i]=list_file[i],a.attributes[list_file[i] ]

let's see what's inside the list_file:

print list_file

['calendar', 'id', 'title', 'production', 'institution', 'Conventions', 'cdms_filemap', 'directory', 'model', 'history']

print file_dic

{0: ('calendar', 'noleap'), 1: ('id', 'none'), 2: ('title', 'AMIP 1 Model Output'), 3: ('production', 'CCSR/NIES AGCM (T21 L20) 1995 '), 4: ('institution', 'Tokyo, Japan'), 5: ('Conventions', ''), 6: ('cdms_filemap', '[[[abs_time,tas,bounds_time],[[0,12,-,-,tas_ccsr-95a_1979.01-1979.12.nc],[12,24,-,-,tas_ccsr-95a_1980.01-1980.12.nc],[24,36,-,-,tas_ccsr-95a_1981.01-1981.12.nc],[36,48,-,-,tas_ccsr-95a_1982.01-1982.12.nc],[48,60,-,-,tas_ccsr-95a_1983.01-1983.12.nc],[60,72,-,-,tas_ccsr-95a_1984.01-1984.12.nc]]],[[bounds_latitude,bounds_longitude,weights_latitude],[[-,-,-,-,tas_ccsr-95a_1979.01-1979.12.nc]]]]'), 7: ('directory', ''), 8: ('model', 'ccsr-95a'), 9: ('history', 'original not LATS-generated have been rewritten in conformance with GDT conventions (described at http://www-pcmdi.gov/drach/GDT_convention.html) [2006-5-5 21:54:46] /home/mccoy20/utils/cdat/bin/cdscan -x tas_cssr.xml tas_ccsr-95a_1979.01-1979.12.nc tas_ccsr-95a_1980.01-1980.12.nc tas_ccsr-95a_1981.01-1981.12.nc tas_ccsr-95a_1982.01-1982.12.nc tas_ccsr-95a_1983.01-1983.12.nc tas_ccsr-95a_1984.01-1984.12.nc')}

and do the same with the variables attributes

#
# rip out data attributes and put them in a dictionary called: data_dic
#
list_data=data.attributes.keys()
data_dic={}
for i in range(0,len(list_data)):
 data_dic[i]=list_data[i],data.attributes[list_data[i] ]

see the list_data

print list_data

['name_in_file', 'missing_value', 'name', 'datatype', 'subgrid', 'long_name', 'units', 'axis']

and in the data_dic

print data_dic

{0: ('name_in_file', 'tas'), 1: ('missing_value', [ 1.00000002e+20,]), 2: ('name', 'tas'), 3: ('datatype', 'Float'), 4: ('subgrid', 'time:mean'), 5: ('long_name', 'Surface Air Temperature'), 6: ('units', 'K'), 7: ('axis', 'TZYX')}

Now we can derive the annual cycle data

#
# ========
#
# calculate Annual Cycle
#
cdutil.setTimeBoundsMonthly(data)
#
start_time = data.getTime().asComponentTime()[0]
end_time = data.getTime().asComponentTime()[-1]
#
ac=cdutil.ANNUALCYCLE.climatology(data(time=(start_time, end_time, 'cob')))
#
# ========

And finaly we can put original data attributes back on the calculated annual cycle data

#
# put original data attributes back on calculated annual cycle data
#
for i in range(0,len(data_dic)):
 dm=data_dic[i]
 setattr(ac,dm[0],dm[1])

Now we can write the data to a NetCDF file

#
# write out file and add global attributes to file
#
o=cdms.open('output.nc','w')
o.write(ac)
for i in range(0,len(file_dic)):
 dm=file_dic[i]
 setattr(o,dm[0],dm[1])

o.close()
a.close()

Here is a dump of the header information from the file 'outpu.nc', we just wrote:


netcdf output {
dimensions:
time = UNLIMITED ; // (12 currently)
bound = 2 ;
zh = 1 ;
latitude = 32 ;
longitude = 64 ;

variables:
double time(time) ;
time:bounds = "bounds_time" ;
time:units = "days since 0" ;
time:calendar = "noleap" ;
time:axis = "T" ;
double bounds_time(time, bound) ;
double zh(zh) ;
zh:positive = "up" ;
zh:long_name = "Height above Earth\'s Surface" ;
zh:units = "m" ;
zh:axis = "Z" ;
double latitude(latitude) ;
latitude:bounds = "bounds_latitude" ;
latitude:long_name = "Latitude" ;
latitude:units = "degrees_north" ;
latitude:axis = "Y" ;
double bounds_latitude(latitude, bound) ;
double longitude(longitude) ;
longitude:bounds = "bounds_longitude" ;
longitude:topology = "circular" ;
longitude:long_name = "Longitude" ;
longitude:units = "degrees_east" ;
longitude:modulo = 360. ;
longitude:axis = "X" ;
double bounds_longitude(longitude, bound) ;
double tas(time, zh, latitude, longitude) ;
tas:missing_value = 1.000000020040877e+20 ;
tas:name = "tas" ;
tas:subgrid = "time:mean" ;
tas:long_name = "Surface Air Temperature" ;
tas:units = "K" ;
tas:axis = "TZYX" ;

// global attributes:
:Conventions = "" ;
:calendar = "noleap" ;
:title = "AMIP 1 Model Output" ;
:production = "CCSR/NIES AGCM (T21 L20) 1995 " ;
:institution = "Tokyo, Japan" ;
:cdms_filemap = "[[[abs_time,tas,bounds_time],[[0,12,-,-,tas_ccsr-95a_1979.01-1979.12.nc],[12,24,-,-,tas_ccsr-95a_1980.01-1980.12.nc],[24,36,-,-,tas_ccsr-95a_1981.01-1981.12.nc],[36,48,-,-,tas_ccsr-95a_1982.01-1982.12.nc],[48,60,-,-,tas_ccsr-95a_1983.01-1983.12.nc],[60,72,-,-,tas_ccsr-95a_1984.01-1984.12.nc]]],[[bounds_latitude,bounds_longitude,weights_latitude],[[-,-,-,-,tas_ccsr-95a_1979.01-1979.12.nc]]]]" ;
:directory = "" ;
:model = "ccsr-95a" ;
:history = "original not LATS-generated have been rewritten in conformance with GDT conventions (described at http://www-pcmdi.gov/drach/GDT_convention.html) [2006-5-5 21:54:46] /home/mccoy20/utils/cdat/bin/cdscan -x tas_cssr.xml tas_ccsr-95a_1979.01-1979.12.nc tas_ccsr-95a_1980.01-1980.12.nc tas_ccsr-95a_1981.01-1981.12.nc tas_ccsr-95a_1982.01-1982.12.nc tas_ccsr-95a_1983.01-1983.12.nc tas_ccsr-95a_1984.01-1984.12.nc" ;
}

This tutorial was provided by Jay Hnilo

	Contents	Previous	Next