Streamlines Tutorial

This tutorial demonstrates VCS streamline support We show randomly seeded and evenly spaced streamlines.

import warnings warnings.filterwarnings('ignore') import vcs import cdms2

The CDAT software was developed by LLNL. This tutorial was written by Charles Doutriaux. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

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Table of Contents

Create Streamlines

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In [1]:
import cdms2
import vcs
In [2]:
# Download the sample data if needed
# vcs.download_sample_data_files()
# read"/")
In [3]:
# read two variables
u = f("u")
v = f("v")
In [4]:
# initialize vcs
In [5]:
# create the streamline graphics method
gm = x.createstreamline()
In [6]:
# we set parameters for randomly seeded streamlines
gm.evenlyspaced = False         # only available on releases after 2.10 or on the nightly packages.
# streamlines are colored by vector magnitude
gm.coloredbyvector = True
# We want 10 glyphs(arrows) per streamline
gm.numberofglyphs = 10
gm.filledglyph = True
# we place 400 random seeds in a circle that covers the data. This means fewer seeds will be inside the data.
# The number of seeds inside the data will result in streamlines.
gm.numberofseeds = 400
In [7]:
# use the robinson projection for the data.
p = x.createprojection()
p.type = 'robinson'
gm.projection = p
In [8]:
# we plot randomly seeded streamlines
x.plot(u, v, gm, bg=1)
In [9]:
# we plot evenly spaced streamlines
gm.evenlyspaced = True # only available only on releases > 2.10 or on the nightly packages
# We want the streamline to be about one cell apart from each other
gm.separatingdistance = 1
# The seed for the first streamline. All other seeds are generated automatically
gm.startseed = [0, 0, 0]
# create an evenly spaced streamline plot
x.plot(u, v, gm, bg=1)