notebook.community

Edit and run



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#hideme
from IPython.core.display import Image 
Image(filename='img/UV-CDAT_logo.png')









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Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)

Designed for climate science data, CDAT was first released in 1997

UV-CDAT was first released in late 2010

Based on the object‐oriented Python computer language

Added Packages that are useful to the climate community and other geophysical sciences
- Climate Data Management System (CDMS)
- NumPy / Masked Array / Metadata
- Visualization (VCS, IaGraphics, Xmgrace, Matplotlib, VTK, Visus, etc.)
- Graphical User Interface (VCDAT)
- XML representation (CDML/NcML) for data sets

One environment from start to finish

Integrated with other packages (i.e., LAS, OPeNDAP, ESG, etc.)

Community Software (BSD open source license)

Links
```
* Ubuntu, CentOS, RedHat & MacOS  
```

Funded by NASA & Department of Energy, U.S.

Climate Data Management System (CDMS)
import cdms2

Best way to ingest and write NetCDF‐CF, HDF, Grib[1 & 2], PP, DRS, Ctl, etc., data!

Opening a file for reading
import cdms2
f = cdms2.open(file_name)
– It will open an existing file protected against writing

Opening a new file for writing
f = cdms2.open(file_name, 'w')
– It will create a new file even if it already exists

Opening an existing file for writing
f = cdms2.open(file_name, 'r+') # or 'a'
– It will open an existing file ready for writing or reading

GUI Intro / Demo

Exercise 2 : Opening a NetCDF file



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import cdms2
DATAPATH = './data/Obs/mo/sst/HadISST/'
# open the nc file throgh cdms2 module
f = cdms2.open(DATAPATH + 'sst_HadISST_Climatology_1961-1990.nc')
# You can query the file
print f.listvariables()



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# You can “access” the data through file variable
x = f['sst']
# You can get some information about the variables by
x.info()



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# or read all of it into memory
y = f('sst') # Causion : It will load the whole data into RAM memory 
y.info()



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# You can also find out what class the object x or y belong to
print x.__class__
# Close the file
f.close() # do remeber we opened the nc file throgh 'f' file object

All the above snippets into single script # demo_1.py



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%load scripts/demo_1.py

Multiple way to retrieve data : Dimension

All of it, omitted dimensions are retrieved entirely
data = f('var')

Specifying dimension type and values
data = f('var', time=(time1,time2))
• Known dimension types : time, level, latitude, longitude (t,z,y,x)

Dimension names and values
data = f('var', dimname1=(val1,val2))

Sometimes indices are more useful than actual values
data = f('var', time=slice(index1, index2, step))

cdtime module

Import the cdtime module



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a = raw_input("emne")



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import cdtime

Relative time



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r = cdtime.reltime(19, "days since 2011-5-1")
r



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dir(r)

Component time



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c = cdtime.comptime(2011, 5, 20)
c



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dir(c)

You can interchange between component and relative time



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c1 = c.torel("days since 2011-1-1")
print "Comptime - ", c
print "Relative - ", c1



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r1 = r.tocomp()
print "Relative - ", r
print "Component - ", r1

Arrays, Masked Arrays and Masked Variables



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#hideme
Image(filename='img/ma.png')









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Arrays, Masked Arrays and Masked Variables



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#hideme
Image(filename='img/ma2.png')









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Summary

Take advantage of NumPy's array syntax to make operations with arrays both faster and more flexible (i.e., act on arrays of arbitrary rank).

Use any one of a number of Python packages (e.g., CDAT, PyNIO, pysclint, PyTables, ScientificPython) to handle netCDF, HDF, etc. files.

Acknowledgments

Dean Williams, Charles Doutriaux (PCMDI, LLNL).
Dr. Johnny Lin (Physics Department, North Park University, Chicago, Illinois).
Dr.Krishna AchtuaRao (Centre for Atmospheic Sciences, Indian Institute of Technology Delhi, India).

License

IPython Notebook Created by

  Arulalan.T 
  Date : 17.02.2014
  Project Associate,
  Centre for Atmospheic Sciences,
  Indian Institute of Technology Delhi, India.
  Blog : http://tuxcoder.wordpress.com 
  Repo : https://github.com/arulalant/UV-CDAT-IPython-Notebooks

This work (IPython Notebook & Html Slide) is licensed under a Creative Commons Attribution‐NonCommercial‐ShareAlike 3.0
Includes all python scripts
Freeware license (only for research purpose, not for commercial) for 'data' directory which contains '.nc' files (any one can download it from TIGGE http://tigge-portal.ecmwf.int/ website)