def test_doGetDataSet():
org = javaaddpath('http://autoplot.org/jnlp/latest/autoplot.jar')
apds = org.autoplot.idlsupport.APDataSet()
apds.setDataSetURI(
'http://autoplot.org/data/swe-np.xls?column=data&depend0=dep0')
apds.doGetDataSet()
vv = apds.values()
assert vv[0] == 3.4716999530792236
def test_APDataSet():
org = javaaddpath('http://autoplot.org/jnlp/latest/autoplot.jar')
apds = org.autoplot.idlsupport.APDataSet()
assert apds is not None
def test_javaaddpath():
org = javaaddpath('http://autoplot.org/jnlp/latest/autoplot.jar')
assert org is not None
# demo how Autoplot library is used to export data. This
# loads an xls file into ndarrays, and then formats the
# data to a CDF file.
from autoplot import javaaddpath, to_ndarray, to_qdataset
# Download autoplot.jar if needed and return Python bridge object
org = javaaddpath('http://autoplot.org/jnlp/latest/autoplot.jar')
# Create Autoplot Data Set
apds = org.autoplot.idlsupport.APDataSet()
# Set URI
apds.setDataSetURI('http://autoplot.org/data/swe-np.xls?column=data&depend0=dep0')
# Get the data
apds.doGetDataSet()
print(apds.toString())
# http://autoplot.org/data/swe-np.xls?column=data&depend0=dep0
# data: data[dep0=288] (dimensionless)
# dep0: dep0[288] (days since 1899-12-30T00:00:00.000Z) (DEPEND_0)
# Extract data values
vv = to_ndarray( apds, 'data' )
tt = to_ndarray( apds, 'dep0' )
# Now export the same data using Autoplot
sc= org.autoplot.ScriptContext
ttqds= to_qdataset( tt )
vvqds= to_qdataset( tt, vv )
import autoplot as ap
org = ap.javaaddpath()
apds = org.autoplot.idlsupport.APDataSet()
apds.loadDataSet('vap+cdaweb:ds=OMNI2_H0_MRG1HR&id=DST1800&timerange=Oct+2016')
epoch = ap.to_ndarray(apds, 'Epoch')
dst = ap.to_ndarray(apds, 'DST')
ap.applot(epoch, dst)
import autoplot as ap
import time
org = ap.javaaddpath('http://autoplot.org/latest/autoplot.jar')
apds = org.autoplot.idlsupport.APDataSet()
apds.loadDataSet('vap+inline:findgen(4000,500)') # that's 2 000 000 elements
t0 = time.time()
dd = ap.to_ndarray(apds, 'ds_0')
print('%.1f s for %s' % (time.time() - t0, str(dd.shape)))
print('----')
apds.loadDataSet('vap+inline:findgen(2000000)') # that's 2 000 000 elements
t0 = time.time()
dd = ap.to_ndarray(apds, 'ds_0')
print('%.1f s for %s' % (time.time() - t0, str(dd.shape)))
print('----')