def test_1d_6hourly_inclusive(self):
"""Should be the same as test_2h_inclusive"""
df_lagged = rolling_mean(self.df.values, '1d', datafreq=6)
nt.assert_array_equal(df_lagged[:3, ], np.nan,
"leading lags should be NAN")
nt.assert_array_equal(df_lagged[3:, ], 0.25,
"Moving average should be constant 1/4")
def get_lagged_df(df, lags=['30min', '2h', '6h', '2d', '7d', '30d', '90d']):
"""Get lagged variants of variables"""
data = {}
for v in df.columns:
data[v] = pd.DataFrame(np.concatenate(
[rolling_mean(df[[v]].values, l) for l in lags], axis=1),
columns=lags,
index=df.index)
return pd.concat(data, axis=1)
def test_2h_exclusive(self):
df_lagged = rolling_mean(self.df.values, '2h', shift=1)
nt.assert_array_equal(df_lagged[:4, ], np.nan,
"leading lags should be NAN")
nt.assert_array_equal(df_lagged[4:, ], 0.25,
"Moving average should be constant 1/4")
def test_30m_exclusive(self):
df_lagged = rolling_mean(self.df.values, '30min', shift=1)
nt.assert_array_equal(df_lagged[:1, ], np.nan,
"leading lags should be NAN")
nt.assert_array_equal(df_lagged[1:, ], self.df.values[:-1, ],
"lagged array should match, but doesn't")
def test_30m_inclusive(self):
df_lagged = rolling_mean(self.df.values, '30min')
nt.assert_array_equal(df_lagged, self.df.values,
"arrays should match, but don't")