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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-pandas. The Analyst compute simple differences between observations and analysis
and use pandas to perform further computations
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)
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1. Analyst retrieves the analysis from a gridded data file
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--------------------------------------------------------------------------------
2. Analyst retrieves an observational parameter from a tabular or a gridded file
--------------------------------------------------------------------------------
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3. Analyst calculates the difference between the observational data and the
analysis
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--------------------------------------------------------------------------------
4. Analyst converts this data to a pandas dataframe and computes the number
of outliers based on the zscore
--------------------------------------------------------------------------------
"""
import metview as mv
import numpy as np
from scipy import stats
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
df = diff.to_dataframe()
print(df)
outliers = np.abs(stats.zscore(df["value"])) > 1.5
print("# of outliers:", outliers.sum())
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