Reading an existing dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^

::

   import icartt

   # load a new dataset from an existing file
   ict = icartt.Dataset('tests/examples/DC8-20160517.ict')

   # list variable names
   [ x for x in ict.variables ]

   # get data for variable 'UTC' (shortcut):
   ict.data['UTC']

   # get all data as NumPy array:
   ict.data.data

   # read some metadata
   ict.PIName
   ict.PIAffiliation
   ict.missionName
   ict.dataSourceDescription

   # some info on a variable
   ict.variables['Alt_ft'].units
   ict.variables['Alt_ft'].miss


Creating a new dataset
^^^^^^^^^^^^^^^^^^^^^^^

::

   import icartt
   import datetime

   ict                                    = icartt.Dataset(format=icartt.Formats.FFI1001)

   ict.PIName                             = 'Knote, Christoph'
   ict.PIAffiliation                      = 'Faculty of Medicine, University Augsburg, Germany'
   ict.dataSourceDescription              = 'Example data'
   ict.missionName                        = 'MBEES'
   ict.dateOfCollection                   = datetime.datetime.today()
   ict.dateOfRevision                     = datetime.datetime.today()

   ict.dataIntervalCode                   = [ 0 ]

   ict.independentVariable                = icartt.Variable(  'Time_Start',
                                                               'seconds_from_0_hours_on_valid_date',
                                                               'Time_Start',
                                                               'Time_Start',
                                                               vartype=icartt.VariableType.IndependentVariable,
                                                               scale=1.0, miss=-9999999)

   ict.dependentVariables['Time_Stop']    = icartt.Variable(   'Time_Stop',
                                                               'seconds_from_0_hours_on_valid_date',
                                                               'Time_Stop',
                                                               'Time_Stop',
                                                               scale=1.0, miss=-9999999)

   ict.dependentVariables['Payload']      = icartt.Variable(   'Payload',
                                                               'some_units',
                                                               'Payload',
                                                               'Payload',
                                                               scale=1.0, miss=-9999999)

   ict.specialComments.append("Some comments on this dataset:")
   ict.specialComments.append("They are just examples!")
   ict.specialComments.append("Adapt as needed.")

   ict.endDefineMode()

   # Three ways to add data:

   # 1) simple (single data line)
   ict.data.add( Time_Start = 12.3, Time_Stop = 12.5, Payload = 23789423.2e5 )

   # Let's check:
   ict.write()

   # 2) as dictionary (single data line)
   mydict = { 'Time_Start': 12.6, 'Time_Stop': 13.1, 'Payload': 324235644.1e5 }
   ict.data.add( **mydict )
   # (note, exploding the dictionary is necessary)

   # 3) as NumPy array (bulk)
   import numpy as np
   data = np.array( [ (13.4, 14.0, 2348925e5), (14.1, 14.9, 23425634e5) ] )
   ict.data.addBulk( data )

   # Note 1: you are responsible to ensure that the order of elements in a data line
   # corresponds to variable listing below:
   print( [ x for x in ict.variables ] )

   # Note 2: for single lines, you still need to make it an array!
   data = np.array( [ (15.4, 15.0, 52452495290e5) ] )
   ict.data.addBulk( data )

   # Now write to file:
   with open('path/to/output.ict', 'w') as f:
      ict.write(f=f)