Transcript Python Frameworks for Geoscience Visualization & Analysis

Python Frameworks
for Geoscience Visualization
and Analysis
SuperComputing ‘05
Seattle, Washington
November 12-18, 2005
Mary Haley
National Center for Atmospheric Research
Mesa Lab  Boulder, Colorado
Here’s what I’m going to tell you
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History lesson
What is Python?
Why we chose Python
Introduction to PyNGL and PyNIO
Examples
Current status
Future plans
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History
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PyNIO
2005
libnio
2005
GSUN
Late 90s
PyNGL
2004
NCL
Mid 90s
HLUs
Mid 90s
PyHLU
2003
C interface
Mid 90s
GKS/CGM/Fortran 77/UNIX conversion
1980s
Original Fortran
Late 60s
What is Python?
A scripting language often compared
with Perl, Tcl, and Java.
Shares similarities with NCL.
Why a Python interface?
• Demand for high-quality 2D
visualization
• Capitalize on the work of others
• Collaboration opportunities
• Mainstream language - wider audience
• Contribution to Python community
• Open source
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Python strengths
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Open source and portable
Full OO capabilities
Well documented and easy to learn
Callable from Java
Toolkits for building GUIs - Tkinter
3D visualization - vtk, OpenGL
Extendable with C/C++ and Fortran
Large and rapidly growing user base
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Python strengths
lots of existing code
• PyTables - manage large amounts of data,
HDF5
• PIL - Python Imaging Library
• Libraries of scientific tools
- FFTPACK, SPHEREPACK, LAPACK
- netCDF module
• PyClimate - tools for climate variability
analysis.
• PyGlobus - toolkit for GRID computing
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“Unlike other simple languages,
Python is not something you
outgrow.”
Ray Pierrehumbert
Professor at University of Chicago
Dept. of Geophysical Sciences
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What is PyNGL?
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Python NCL Graphics Library
Python version of popular GSUN interface
Some data processing
Same publication-quality graphics as NCL
Utilizes existing Python modules
Multiple output formats
Extensive documentation
http://www.pyngl.ucar.edu/
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What is PyNIO?
• A multi-format IO module
• Modelled after Konrad Hinson’s NetCDF
module
• A unified NetCDF-like view of all formats
• Reads NetCDF, GRIB 1, HDF 4 (SDS),
HDFEOS 2 (Grid and Swath), CCM
• Writes NetCDF, HDF 4
• Packaged with PyNGL for now, eventually
stand-alone
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filename:
ruc2.T00Z.bgrbanl
path: /Users/dbrown/grib/ruc2.T00Z.bgrbanl.grb
file global attributes:
dimensions:
gridx_236 = 113
gridy_236 = 151
lv_HYBL2 = 40
lv_DBLL3 = 5
variables:
float PRES_236_TRO ( gridx_236, gridy_236 )
center :
US National Weather Service - NCEP (WMC)
long_name : Pressure
units :
Pa
_FillValue : -999
coordinates : gridlat_236 gridlon_236
level_indicator :
7
grid_number : 236
parameter_number : 1
model :
RUC Model from FSL (isentropic; scale: 20km at 40N)
forecast_time :
0
initial_time : 05/11/2000 (00:00)
…
float gridlat_236 ( gridx_236, gridy_236 )
corners :
<ARRAY>
long_name : latitude
GridType : Lambert Conformal Secant or Tangent, Conical or bipolar
units :
degrees_north
…
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PyNGL and PyNIO examples
• Xy, contours, skew-t, meteograms,
maps
• Interpolation to rectangular grid
• Triangular grid
• Vector plot of ocean data
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PyNGL/PyNIO
NCL/GSUN
import Ngl, Nio
load "gsn_code.ncl"
# Open the NetCDF file.
nf = Nio.open_file("mtemp.cdf","r")
begin
; Open the NetCDF file.
nf = addfile("mtemp.cdf","r")
# Get
lat =
lon =
T
=
; Get lat/lon/temperature variables.
lat = nf->lat
lon = nf->lon
T
= nf->t(0,:,:)
lat/lon/temperature variables.
nf.variables["lat"][:]
nf.variables["lon"][:]
nf.variables["t"][0,:,:]
# Open a PS workstation.
wks = Ngl.open_wks("ps","mecca")
; Open a PS workstation.
wks = gsn_open_wks("ps","mecca")
# Contour & scalar
res
res.sfXArray
res.sfYArray
res.cnFillOn
; Contour & scalar field resources.
res
= True
res@sfXArray
= lon
res@sfYArray
= lat
res@cnFillOn
= True
res@lbPerimOn
= False
field resources.
= Ngl.Resources()
= lon
= lat
= True
# Draw contour plot.
contour = Ngl.contour(wks,T,res)
Ngl.end()
; Draw contour plot.
contour = gsn_contour(wks,T,res)
end
Data from the MECCA Project
Skew--T Courtesy Dennis Shea
Data from the MECCA Project
Data from the MECCA Project
Courtesy Sylvia Murphy, NCAR ACD
From John Ertl, FNMOC
Based on a visualization of Joel Norris (Scripps) using dummy data
Climate division data courtesy Mark Stevens, NCAR CGD
Courtesy Mark Stevens, NCAR CGD
New types of grids you can contour
• Grids already on a triangular mesh
• Grids represented by one-dimensional
arrays of x, y, and z values
• Grids with “cell boundary” information
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Grid from Tom Gross, NOAA/NOS/CSDL/MMAP
Grid from Tom Gross, NOAA/NOS/CSDL/MMAP
GridBrett
fromD.Brett
D. Estrada,
Naval Research
Grid from
Estrada,
Naval Research
LaboratoryLaboratory
Grid from Brett D. Estrada, Naval Research Laboratory
HOMME grid courtesy of Steve Thomas, SCD
HOMME grid courtesy of Steve Thomas, SCD
HOMME grid courtesy of Steve Thomas, SCD
Grid and data from Dave Randall, CSU
Grid and data from Dave Randall, CSU
http://isccp.giss.nasa.gov/
http://isccp.giss.nasa.gov/
Christophe Cassou (CNRS/CERFACS)
Interpolation examples
using natgrid and
Delauney triangulation
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Current status
• PyNGL/PyNIO beta 0.1.1b8 available
• Binaries (easy installation) available for:
– Various flavors of Linux, Mac OSX,
– Other Unix systems, 32 and 64 bit
http://www.pyngl.ucar.edu/download.html
• New functions and examples
• Extensive documentation
• Revamping examples and documentation
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Future plans
• PyNGL/PyNIO 1.0 by the end of the year
• More functions and examples
• More IO formats and functionality:
– NetCDF 4, GRIB 2, HDF/HDFEOS 5
– Support for groups, structures, etc.
– Datasets aggregated from multiple files
• Long term - a better display model
• Open source (currently distributed as binary)
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Questions?
• PyNGL
– http://www.pyngl.ucar.edu
– Tutorial, examples with line-by-line
descriptions, function reference
• PyNIO
– http://www.pyngl.ucar.edu/Nio.html
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