Ex 02: Simple Cubic SpectraΒΆ

Plotting simple cubic spectra from the Galileo PWI using pcolormesh.

_images/ex02_galileo_pws_spectra.png 
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# das2 module example 2:
#    Plotting simple cubic Galileo PWS spectra

import das2
import das2.mpl  # Extra helpers for interfacing to matplotlib

import matplotlib.pyplot as pyplot
import matplotlib.colors as colors
import matplotlib.dates  as dates

# Get a data source definition from the federated catalog system.  The ID below
# should not change due to server outages or dataset migration so long as the
# catalog is updated.  Each ID can map to mutiple URLs for automatic failover.
# In reality a valid URI is transmitted on the wire, not just the ID below.
# Das2py knows to prepend 'tag:das2.org:2012:' automatically when looking for
# data sources if this prefix is not present.

sId = "site:/uiowa/galileo/pws/survey_electric/das2"
print("Getting data source definition for %s"%sId)
src = das2.get_source(sId)

# Get data from the source.  Note, if no parameters are specifed the default
# range and resolution in the source definition will be used
print("Reading default example Galileo PWS E-Survey data...")
lDs = src.get()
ds = lDs[0]     # simple example, real code should check dataset list size
print("%d datasets returned"%len(lDs))

# Optionally, print a summary of the dataset
#print(ds)

# The dataset object returned by the data source is a bit more complex than a
# CDF object, but simpler than an HDF database.  It was designed to provide
# explicit details regarding the layout and purpose each variable without
# conflating iteration index space with real physical dimensions (though the two
# sets may happen to be related)

time = ds['time']         # These represent real-world physical dimesions.
freq = ds['frequency']    # Dimensions have 1 or more variables (below)
specDens = ds['electric'] # that provide data values.

# das2py Variables provide ndarray views for accessing data.  Every Variable
# in the Dataset has the same bulk iteration properites.  They have the same
# index ranges for the same index dimensions... always.  Said another way,
# Datasets consist of Variables correlated in index space.  So:
#
#   time['center'][i,j] is the time value for
#
#   freq['center'][i,j] is the frequency value for
#
#   specDens['center'][i,j].
#
# Always.  No matter the internal data storage model.
#
# By providing a single general interface (scatter data), special case handling
# is avoided.

# The pcolormesh plotter from matplotlib works well enough with this data model.
# But a general re-binning plotter should be used instead.
aX = time['center'].array
aY = freq['center'].array
aZ = specDens['center'].array

if specDens.propEq('scaleType','log'):
	clrscale = colors.LogNorm(vmin=aZ.min(), vmax=aZ.max())
else:
	clrscale = None

(fig, ax0) = pyplot.subplots()

im = ax0.pcolormesh(aX, aY, aZ, norm=clrscale, cmap='jet' )
cbar = fig.colorbar(im, ax=ax0)

if freq.propEq('scaleType','log'):
	ax0.set_yscale('log')

fig.autofmt_xdate()  # Fix date formating
ax0.fmt_xdata = dates.DateFormatter("%Y-%m-%dT%H:%M")  # High-Res in mouse over
ax0.xaxis.set_minor_locator(dates.MinuteLocator(interval=5)) # add minor ticks

# Set plot labels, will use matplotlib helpers from das2 module to format labels
ax0.set_xlabel(das2.mpl.range(time) )
ax0.set_ylabel(das2.mpl.text(freq.props['label']))
cbar.set_label(das2.mpl.text(specDens.props['label']) )

ax0.set_title(das2.mpl.text(ds.props['title']) )

pyplot.show()