Python

Download extra packages

Download the libraries

If you have Anaconda installed, you should install with pip. In Linux, you can run pip on a regular shell. In Windows you will need to run on the Anaconda Prompt shell: click Start and search or select Anaconda Prompt from the menu. Run this command in the same folder where the file sdas-1.0.tar.gz is located, or use the full path:

pip install sdas-1.0.tar.gz

If you don't have Anaconda (or for some reason pip doesn't work), you can unpack sdas-1.0.tar.gz and run:

python setup.py install

You may need to have root or administrator privileges.

Another option  is to copy the sdas folder, inside the package, to the same folder where you have your code.

Get a connection to the sdas server

from sdas.core.client.SDASClient import SDASClient
from sdas.core.SDAStime import Date, Time, TimeStamp
host='baco.ipfn.ist.utl.pt'
port=8888
client = SDASClient(host,port)

Search events

found = client.searchDeclaredEventsByName('S')

found = client.searchDeclaredEventsByName('SHOT', 'pt')

found = client.searchDeclaredEventsByUniqueID('SHOT', 'pt')

found = client.searchDeclaredEventsByDescription('SHOT')

found = client.searchDeclaredEventsByDescription('SHOT', 'pt')

for item in found:
    print 'item', item
max = client.searchMaxEventNumber('0x0000')

min = client.searchMinEventNumber('0x0000')

Search parameters

parametersFound = client.searchParametersByName('DENS')

parametersFound = client.searchParametersByName('DENS', 'pt')

parametersFound = client.searchParametersByUniqueID('DENS')

for p in parametersFound:
    print p['descriptorUID']['uniqueID']

Search data

dataFound = client.searchDataByEvent('0x0000', 17898)
for d in dataFound:
    print d

Get data

NOTE: The unique identifiers are CASE-SENSITIVE

The returned data structure gives you information about:

• start time
• end time
• time of the event
• mime_type
• the parameter unique identifier

Import extra libraries

import numpy
import matplotlib.pyplot as plt

Data for only one parameter

dataStructArray=client.getData('POST.PROCESSED.DENSITY','0x0000', 17898)
dataStruct=dataStructArray[0]
density=dataStruct.getData()
tstart = dataStruct.getTStart()
tend = dataStruct.getTEnd()

Calculate the time between samples

tbs = (tend.getTimeInMicros() - tstart.getTimeInMicros())/len(density)

Get the events  associated with this data

events = dataStruct.get('events')

The event time (I’m assuming the event I want is at the index 0, but I should check first...)

tevent = TimeStamp(tstamp=events[0].get('tstamp'))

The delay of the start time relative to the event time

delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()

Finally create the time array

times = numpy.linspace(delay,delay+tbs*(len(density)-1),len(density))

And plot the data

plt.plot(times, density)

If the plot doesn't show, you may need to run.

plt.show()