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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Working with Fieldsets

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: 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)
• iteration works: for f in my_fieldset:  #do something
  

Working with Geopoints

Geopoints also work much the same as they do in Macro, but be aware of these points:

• in Macro, we use geo_missing_value to denote missing data values; in Python, we use numpy.nan

Working with dates

There are several differences between the usage of the date object in Macro and the datetime object in Python. You will find a few examples below to compare the various date manipulation techniques used in Macro and Python, respectively.

# 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 = mv.hour(d), ...)	
	(...)	

Additional data export features

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Any Metview function that normally returns a vector will return a numPy array when called from Python. dtypes of float32 and float64 are supported. For example, the follownig following fieldset functions return numPy arrays:

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<xarray.Dataset>
Dimensions:    (latitude: 181, longitude: 360, step: 9, time: 1)
Coordinates:
  * time       (time) datetime64[ns] 2018-05-10T12:00:00
  * step       (step) timedelta64[ns] 0 days 00:00:00 0 days 06:00:00 ...
  * latitude   (latitude) float64 90.0 89.0 88.0 87.0 86.0 85.0 84.0 83.0 ...
  * longitude  (longitude) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 ...
Data variables:
    2t         (time, step, latitude, longitude) float32 ...
    2d         (time, step, latitude, longitude) float32 ...
Attributes:
    Conventions:  CF-1.7
    comment:      GRIB to CF translation performed by xarray-grib

This is based on the xarray GRIB driver, which is not yet available outside of Metview, but is planned to be released and ultimately integrated into xarray.Open Source cfgrib package which is planned to be ultimately integrated into xarray.

From version 0.8.6 of Metview's Python interface, it is also possible to convert an xarray dataset to a Metview Fieldset, and also to pass one directly to Metview functions. Note that this uses an experimental feature of cfgrib and will only work for a small subsset of xarray datasets. Examples:

grib = mv.read('temperature_on_pl.grib')
x = grib.to_dataset() # convert from Fieldset to dataset
f = mv.dataset_to_fieldset(x) # convert back to Fieldset

grib = mv.read('temperature_on_pl.grib')
x = grib.to_dataset() # x is now an xarray dataset
fs = mv.mean(x*2) # *2 is done by xarray, mean() is done by Metview

Icon functions

Macro functions which correspond to icons, such as retrieve(), which corresponds to the Mars Retrieval icon, can take their arguments in a number of ways:

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• Macro indexing starts at 1, but Python indexing starts at 0. Aside from the standard Python structures such as lists, this is also true for Metview Python classes such as Fieldset. Given a fieldset fs, the first field is obtained in Macro by fs[1], but in Python it is obtained by fs[0].
• Some MARS requests require a class parameter. As class is a restricted keyword in Python, please use class_ in its place.
• In order to support the more interactive coding environments provided by Python, any call to the plot() command will immediately produce a plot. This is different from Macro, where plots are delayed until the end. The result of this is that multiple plot() commands in Python will result in multiple plot windows. Fortunately, a single plot() command can be given any number of items, including multiple pages. To see how to produce a multi-plot layout, please see the Layoutx3 Example gallery example.
• To produce multiple physical pages in a PostScript file, add calls to newpage() within the plot() command, e.g. plot(dw, data1, visdef1, newpage(), data2, visdef2).