def test_load_data(tBeg, tEnd, SrcId):
"""tests whether data exists for the given time range to avoid IndexError issue."""
try:
src = das2.get_source(SrcId)
dQuery = {'time':(tBeg, tEnd), 'hfr_i':True}
dsReal = src.get(dQuery)[0]
except (IndexError):
return False
else:
return True
def getIQ(sSrcId, sBeg, sEnd):
"Returns the tuple (dataset I, dataset Q)"""
src = das2.get_source(sSrcId)
print("Getting Reals from %s to %s"%(sBeg, sEnd))
dQuery = {'time':(sBeg, sEnd), 'hfr_i':True}
dsReal = src.get(dQuery)[0]
print(dsReal)
print("Getting Imaginary from %s to %s"%(sBeg, sEnd))
dQuery = {'time':(sBeg, sEnd), 'hfr_q':True}
dsImg = src.get(dQuery)[0]
#print(dsImg)
return (dsReal, dsImg, src)
# No she-bang here because we want the test target to pick the python version
import sys
import das2
#src = das2.get_source('site:/uiowa/juno/wav/survey/das2')
src = das2.get_source('site:/uiowa/cassini/rpws/survey_keyparam/das2')
lDs = src.httpGet({'start_time':'2016-10-02T11:00', 'end_time':'2016-10-02T12:00'})
for i in range(len(lDs)):
print("Dataset 1:")
print(lDs[i].info)
lDs = src.httpGet({'start_time':'2016-10-02T11:00', 'end_time':'2016-10-02T12:00'})
sys.exit(117)
src = das2.get_source('site:/uiowa/cassini/rpws/survey_keyparam/das2')
lDs = src.get(time=('2010-001','2010-002'), magnetic_specdens=1)
## For data items we want to be able to change units and to enable or
## disable various entries
#
lDs = src.get(time=('2014-08-31', '2014-09-01', 43.2), amp={'units':'DN'})
#
import numpy
import das2
import matplotlib.pyplot as pyplot
import matplotlib.colors as colors
import matplotlib.ticker as ticker
# Use the federated das2 catalog to find and download data given a source ID
sSourceId = "tag:das2.org,2012:site:/uiowa/cassini/rpws/survey/das2"
src = das2.get_source(sSourceId)
lDatasets = src.get()
# Combining multiple table datasets into a single homogeneous scatter dataset
# (most datasets are homogeneous and will not require this step)
lX = []
lY = []
lZ = []
for dataset in lDatasets:
# As of numpy version 1.15, the numpy histogram routines won't work
# with datetime64, and timedelta64 data types. To Working around a
# this problem time data are cast to the int64 type giving time values
# as integer nanoseconds since 1970.
lX.append(dataset.coords['time']['center'][:,:].flatten().astype("int64"))
lY.append(dataset.coords['frequency']['center'][:,:].flatten() )
lZ.append(dataset.data['amplitude']['center'][:,:].flatten() )
aX = numpy.ma.concatenate(lX)
aY = numpy.ma.concatenate(lY)
aZ = numpy.ma.concatenate(lZ)
import das2
import das2.mpl
import matplotlib.pyplot as pyplot
import matplotlib.colors as colors
import matplotlib.ticker as ticker
# The Cassini RPWS Survey data source is one of the most complicated das2 data
# sources at U. Iowa. Data consist of multiple frequency sets from multiple
# recivers all attempting to cover the maximum parameter space within a limited
# telemetry alotment and on-board computing power. This example collapses
# all the provided datasets to a single scatter set for plotting
sId = "site:/uiowa/cassini/rpws/survey/das2"
print("Getting data source definition for %s" % sId)
src = das2.get_source(sId)
print("Reading default example Cassini RPWS E-Survey data...")
lDs = src.get()
# Combining multiple spectra into one overall array set
print("Combining arrays...")
lX = []
lY = []
lZ = []
for ds in lDs:
# As of numpy version 1.15, the numpy histogram routines won't work
# with datetime64, and timedelta64 data types. To Working around a
# this problem time data are cast to the int64 type
lX.append(ds['time']['center'].array.flatten().astype("int64"))
def main():
# get a datasource, use it to download data
sId = "test:/uiowa/mars_express/marsis/ne-density-planetographic/das2"
src = das2.get_source(sId)
print(src.info())
beg = '2014-01-01'
end = '2016-01-01'
r_alt_max = 2000
theta_sza_max = 135
#query = {'alt':(0, 2000), 'sza':(0, theta_sza_max), 'time':(beg, end)}
query = {'alt': (0, 2000), 'time': (beg, end)}
datasets = src.get(query, verbose=True)
ds = datasets[0]
print(ds)
# access the physical dimensions and numpy arrays in the dataset
alt_dim = ds['alt'] # The altitude dimension
alt_ary = ds.array('alt') # equavalent to: ds['alt']['center'].array
sza_dim = ds['sza'] # The solar zenith angle dimension
sza_ary = ds.array('sza') * (np.pi / 180.0) # scale to radians
Ne_dim = ds['dens'] # The plasma density dimension
Ne_ary = ds.array('dens')
# Plotting...
fig = pyplot.figure(figsize=(6, 4))
loc_in_fig = [0.03, 0.0, 0.9, 0.9]
r_offset = 1000
title = "MARSIS - Plasma Density by Solar Zenith Angle and Altitude"
sub_title = "%s to %s, SZA max %d$^\\circ$, expanded altitude scale" % (
beg, end, theta_sza_max)
theta_label = das2.mpl.label(sza_dim.props['label'])
rad_label = das2.mpl.label(alt_dim.props['label'])
cart_ax, pol_ax = make_axes(fig, loc_in_fig, r_alt_max, r_offset, title,
sub_title, theta_label, rad_label)
# HexBin doesn't seem to work in polar space, use the cartesian axes
x_ary, y_ary = to_cartesian(sza_ary, alt_ary, r_offset)
color_scale = colors.LogNorm(10, 20000)
hb = cart_ax.hexbin(x_ary,
y_ary,
Ne_ary,
gridsize=120,
mincnt=1,
norm=color_scale,
bins=None,
cmap='jet')
# Colorbar axis
density_label = "$\mathregular{N_{e}\\ (cm^{-3})}$"
color_ax = fig.add_axes([0.87, 0.225, 0.02, 0.425])
color_ax.text(3.0,
0.5,
density_label,
ha='center',
va='center',
transform=color_ax.transAxes,
rotation=90)
fig.colorbar(hb, cax=color_ax)
pyplot.show()