Metview's Python interface provides access to all of Metview's Macro language functions, with automatic translations between data types. Here's an example that shows how to retrieve model and observation data and compute their difference, and the weighted mean value of that difference, both in Macro and in Python.

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Fieldsets work much the same as they do in the Macro language, but watch out for these things:

• length of a fieldset can be found with the len function: num_fields = len(my_fieldset)
• indexing starts at 0: first_field = my_fieldset[0]
• slicing works: my_fields = fs[0:6:2]
• you can pass a numpy array of indexes: my_fields = fs[np.array([1.0, 2.0, 0.0, 5.0])]
• comparison operators work the same as in Macro, i.e. they return a fieldset of 1s and 0s: comparison operators work the same, i.e. they return a fieldset of 1s and 0s: smaller = fs1 < fs2
• equality and non-equality operators are == and !=
• Fieldsets can be directly constructed either as empty, or with a path to a GRIB file:
  • f = mv.Fieldset()
  • f = mv.Fieldset(path='test.grib')
    
• concatenation can be done like this:  my_fieldset.append(my_other_fieldset)
• length of a fieldset can be found with the len function: num_fields = len(my_fieldset)
• slicing works: my_fields = fs[0:6:2]
• • 'test.grib')
    
• concatenation can be done like this:  my_fieldset.append(my_other_fieldset)you can pass a numpy array of indexes: my_fields = fs[np.array([1.0, 2.0, 0.0, 5.0])]
• iteration works: for f in my_fieldset:  #do something
  

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# Metview Macro



# creating
d = 2000-01-04 09:50:24 
today = date(0)
yesterday = date(-1) 

# arithmetic
d = d + hour(9) + minute(50) + second(24) 


# using an increment of 2 days
for d = 2018-11-01 to 2018-11-10 by 2 do
  (...)
end for 
 



# using an increment of 6 hours
for d = 2018-11-01 to 2018-11-10 by hour(6) do
   x = retrieve(
      date : yymmdd(d),
      time : hhmm(d),
      ...)
   (...)
end for

import metview as mv
import numpy as np
from datetime import datetime, timedelta

# creating 
d = datetime(2000, 1, 4, 9, 50, 24)
today = datetime.today()
yesterday = datetime.today() - timedelta(1) 

# arithmetic
d = d + timedelta(hours=9, minutes=50, seconds=24) 


# using an increment of 2 days
d0 = datetime(2018,11,1)
d1 = datetime(2018,11,10)
dt = timedelta(days = 2)
for d in np.arange(d0, d1, dt):
    (...)


# using an increment of 6 hours
d0 = datetime(2018, 11, 1)
d1 = datetime(2018, 11, 10)
dt = timedelta(hours = 6)
for d = np.arange(d0, d1, dt):
	x = mv.retrieve(date =d, 
			time = dmv.hour(d), ...)	
	(...)	

Additional data export features

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