def test_cmov_window(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
vals = np.random.randn(10)
xp = cmov_window(vals, 5, 'boxcar')
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert_almost_equal(xp.compressed(), rs[2:-2])
assert_almost_equal(xp.mask, np.isnan(rs))
xp = Series(rs)
rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
assert_series_equal(xp, rs)
def test_cmov_window(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
vals = np.random.randn(10)
xp = cmov_window(vals, 5, 'boxcar')
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert_almost_equal(xp.compressed(), rs[2:-2])
assert_almost_equal(xp.mask, np.isnan(rs))
xp = Series(rs)
rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
assert_series_equal(xp, rs)
def test_cmov_window_special(self):
_skip_if_no_scipy()
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{
'beta': 1.
}, {
'std': 1.
}, {
'power': 2.,
'width': 2.
}, {
'width': 0.5
}]
for wt, k in zip(win_types, kwds):
vals = np.random.randn(10)
xp = cmov_window(vals, 5, (wt, ) + tuple(k.values()))
rs = mom.rolling_window(Series(vals), 5, wt, center=True, **k)
assert_series_equal(Series(xp), rs)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index,
major_axis=df.columns,
minor_axis=df.columns)
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(
x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(
x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def test_cmov_window_frame(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# DataFrame
vals = np.random.randn(10, 2)
xp = cmov_window(vals, 5, 'boxcar')
rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_frame(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# DataFrame
vals = np.random.randn(10, 2)
xp = cmov_window(vals, 5, 'boxcar')
rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_frame(self):
tm._skip_if_no_scipy()
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest("no scikits.timeseries")
# DataFrame
vals = np.random.randn(10, 2)
xp = cmov_window(vals, 5, "boxcar")
rs = mom.rolling_window(DataFrame(vals), 5, "boxcar", center=True)
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_corner(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assert_(np.isnan(rs).all())
# empty
vals = np.array([])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assert_(len(rs) == 0)
# shorter than window
vals = np.random.randn(5)
rs = mom.rolling_window(vals, 10, 'boxcar')
self.assert_(np.isnan(rs).all())
self.assert_(len(rs) == 5)
def test_cmov_window_corner(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assert_(np.isnan(rs).all())
# empty
vals = np.array([])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assert_(len(rs) == 0)
# shorter than window
vals = np.random.randn(5)
rs = mom.rolling_window(vals, 10, 'boxcar')
self.assert_(np.isnan(rs).all())
self.assert_(len(rs) == 5)
def test_cmov_window_regular(self):
tm._skip_if_no_scipy()
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest("no scikits.timeseries")
win_types = ["triang", "blackman", "hamming", "bartlett", "bohman", "blackmanharris", "nuttall", "barthann"]
for wt in win_types:
vals = np.random.randn(10)
xp = cmov_window(vals, 5, wt)
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(Series(xp), rs)
def test_cmov_window_corner(self):
tm._skip_if_no_scipy()
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest("no scikits.timeseries")
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
rs = mom.rolling_window(vals, 5, "boxcar", center=True)
self.assertTrue(np.isnan(rs).all())
# empty
vals = np.array([])
rs = mom.rolling_window(vals, 5, "boxcar", center=True)
self.assertEqual(len(rs), 0)
# shorter than window
vals = np.random.randn(5)
rs = mom.rolling_window(vals, 10, "boxcar")
self.assertTrue(np.isnan(rs).all())
self.assertEqual(len(rs), 5)
def test_cmov_window_regular(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
for wt in win_types:
vals = np.random.randn(10)
xp = cmov_window(vals, 5, wt)
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(Series(xp), rs)
def test_cmov_window_regular(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
for wt in win_types:
vals = np.random.randn(10)
xp = cmov_window(vals, 5, wt)
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(Series(xp), rs)
def test_cmov_window_na_min_periods(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# min_periods
vals = Series(np.random.randn(10))
vals[4] = np.nan
vals[8] = np.nan
xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_na_min_periods(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
# min_periods
vals = Series(np.random.randn(10))
vals[4] = np.nan
vals[8] = np.nan
xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_special(self):
tm._skip_if_no_scipy()
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest("no scikits.timeseries")
win_types = ["kaiser", "gaussian", "general_gaussian", "slepian"]
kwds = [{"beta": 1.0}, {"std": 1.0}, {"power": 2.0, "width": 2.0}, {"width": 0.5}]
for wt, k in zip(win_types, kwds):
vals = np.random.randn(10)
xp = cmov_window(vals, 5, (wt,) + tuple(k.values()))
rs = mom.rolling_window(Series(vals), 5, wt, center=True, **k)
assert_series_equal(Series(xp), rs)
def test_cmov_window_special(self):
try:
from scikits.timeseries.lib import cmov_window
except ImportError:
raise nose.SkipTest
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta' : 1.}, {'std' : 1.}, {'power' : 2., 'width' : 2.},
{'width' : 0.5}]
for wt, k in zip(win_types, kwds):
vals = np.random.randn(10)
xp = cmov_window(vals, 5, (wt,) + tuple(k.values()))
rs = mom.rolling_window(Series(vals), 5, wt, center=True,
**k)
assert_series_equal(Series(xp), rs)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def plot_window(win_type):
df2 = rolling_window(df, 22, win_type)
df2.columns = [win_type]
df2.plot(ax=ax)
def plot_window(win_type):
df2 = rolling_window(df, 22, win_type)
df2.columns = [win_type]
df2.plot(ax=ax)
