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"""
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
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"""

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())