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# (C) Copyright 2017- ECMWF.
#
# This software is licensed under the terms of the Apache Licence Version 2.0
# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
#
# In applying this licence, ECMWF does not waive the privileges and immunities
# granted to it by virtue of its status as an intergovernmental organisation
# nor does it submit to any jurisdiction.
"""
Metview Python use case
UC-07. The Analyst compute simple differences between observations and analysis
and plot the values
BUFR version - BUFR is not tabular or gridded, but we can use Metview Python
framework to extract a particular parameter to a tabular format (geopoints)
--------------------------------------------------------------------------------
1. Analyst retrieves the analysis from a gridded data file
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
2. Analyst retrieves an observational parameter from a tabular or a gridded file
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
3. Analyst calculates the difference between the observational data and the
analysis and classified the field values according to the magnitude of the
difference
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
4. Analyst customises many features of his graph in order to create
publication-quality plots
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
5. Analyst plots the data
--------------------------------------------------------------------------------
"""
import metview as mv
# define a view over the area of interest
area_view = mv.geoview(map_area_definition="corners", area=[45.83, -13.87, 62.03, 8.92])
t2m_grib = mv.read("./t2m_grib.grib")
obs_3day = mv.read("./obs_3day.bufr")
t2m_gpt = mv.obsfilter(parameter="012004", output="geopoints", data=obs_3day)
diff = t2m_grib - t2m_gpt
diff_symb = mv.msymb(
legend=True,
symbol_type="marker",
symbol_table_mode="advanced",
)
mv.setoutput(mv.png_output(output_width=1000, output_name="./obsdiff1"))
mv.plot(area_view, diff, diff_symb)
# Extract geopoints that are hotter by 1 deg or more
# hotter = mv.filter(diff, diff >= 1)
hotter = diff.filter(diff >= 1)
# Extract geopoints that are colder by 1 deg or more
# colder = mv.filter(diff, diff <= -1)
colder = diff.filter(diff <= -1)
# Get geopoints that are within +/-1
# exact = mv.filter(diff, (diff > -1) * (diff < 1))
exact = diff.filter((diff > -1) * (diff < 1))
# Symbol visdefs for each classification
red = mv.msymb(symbol_type="marker", symbol_colour="red")
blue = mv.msymb(symbol_type="marker", symbol_colour="blue")
grey = mv.msymb(symbol_type="marker", symbol_colour="grey")
# plot the 'exact' data set with visdef 'grey', 'hotter' with 'red', etc.
mv.setoutput(mv.png_output(output_width=1000, output_name="./obsdiff2"))
mv.plot(area_view, exact, grey, hotter, red, colder, blue)
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