import numpy as np
import pandas
from star import Stark
if __name__ == '__main__' :
np.random.seed()
df = pandas.DataFrame({
'YEAR': pandas.to_datetime(
[str(year) for year in np.arange(2005, 2013)] * 3) ,
'XER': ['ITA'] * 8 + ['FRA'] * 8 + ['DEU'] * 8,
'X': np.random.randn(24),
'K': np.random.randn(24),
})
stk = Stark(df)
stk.md['vars']['YEAR']['type'] = 'D'
stk.md['vars']['XER']['type'] = 'D'
stk.md['vars']['X']['type'] = 'N'
stk.md['vars']['K']['type'] = 'N'
stk['OUT'] = '$X / $K * 1000'
stk.cagr('X')
stk.cagr('OUT')
stk.logit('K')
df1 = pandas.DataFrame({
'XER': ['ITA', 'FRA', 'ESP'],
'K': np.random.randn(3),
'Q': np.random.randn(3),
})
# -*- coding: utf-8 -*-
# pylint: disable=W0212
import numpy as np
import pandas
import star
from star import Stark
if __name__ == '__main__' :
df = pandas.DataFrame({
'YEAR': pandas.to_datetime([str(year) for year in np.arange(1995, 2013)]),
'XER': ['ITA', 'FRA', 'DEU'] * 6,
'X': np.random.randn(18),
'K': np.random.randn(18),
})
stk = Stark(df)
stk._md['vars']['YEAR']['type'] = 'D'
stk._md['vars']['XER']['type'] = 'D'
stk._md['vars']['X']['type'] = 'N'
stk._md['vars']['K']['type'] = 'N'
stk['OUT'] = '$X / $K * 1000'
out = stk.rollup(YEAR='TOT')['OUT']
assert(type(out._md) is star.share.meta_dict.Meta)
assert(type(out._md['vars']) is star.share.meta_dict.MetaVars)
assert(type(out._md['vars']['OUT']) is star.share.meta_dict.MetaVarsAttr,)
assert(type(out._md['graph']) is star.share.meta_dict.MetaGraph)
assert(type(out._md['graph']['vars']) is star.share.meta_dict.MetaVarsGraph)
# assert(type(out._md['graph']['vars']['OUT']) is star.share.meta_dict.MetaVarsAttrGraph)