def moving_average(Ser,
window_size=1):
'''
Applies a moving average (box) filter on an input time series
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
window_size : float, optional
The size of the moving_average window [days] that will be applied on the
input Series
Default: 1
Returns
-------
Ser : pandas.Series
moving-average filtered time series
'''
# if index is datetimeindex then convert it to julian date
if type(Ser.index) == pd.DatetimeIndex:
jd_index = julday(Ser.index.month, Ser.index.day, Ser.index.year,
Ser.index.hour, Ser.index.minute, Ser.index.second)
else:
jd_index = Ser.index.values
filtered = boxcar_filter(np.squeeze(Ser.values.astype(np.double)), jd_index.astype(np.double), window=window_size)
result = pd.Series(filtered, index=Ser.index)
return result
def test_boxcar_filter_arrsize_1():
"""
Test boxcar filter with array of size 1
"""
test_jd = np.arange(1, dtype=np.double)
test_data = np.array([1], dtype=np.double)
filtered = filters.boxcar_filter(test_data, test_jd, window=5)
np.testing.assert_allclose(filtered, [1.])
def test_boxcar_filter_arrsize_1():
"""
Test boxcar filter with array of size 1
"""
test_jd = np.arange(1, dtype=np.double)
test_data = np.array(
[1], dtype=np.double)
filtered = filters.boxcar_filter(test_data, test_jd, window=5)
np.testing.assert_allclose(filtered, [1.])
def test_boxcar_filter():
"""
Test boxcar filter
"""
test_jd = np.arange(10, dtype=np.double)
test_data = np.array(
[1, 2, 3, 4, -999999.0, 6, 7, 8, 9, np.nan], dtype=np.double)
filtered = filters.boxcar_filter(test_data, test_jd, window=5)
np.testing.assert_allclose(filtered, [2., 2.5, 2.5, 3.75, np.nan, 6.25,
7.5, 7.5, 8., np.nan])
def test_boxcar_filter():
"""
Test boxcar filter
"""
test_jd = np.arange(10, dtype=np.double)
test_data = np.array(
[1, 2, 3, 4, -999999.0, 6, 7, 8, 9, np.nan], dtype=np.double)
filtered = filters.boxcar_filter(test_data, test_jd, window=5)
np.testing.assert_allclose(filtered, [2., 2.5, 2.5, 3.75, np.nan, 6.25,
7.5, 7.5, 8., np.nan])
def moving_average(Ser,
window_size=1,
fillna=False,
min_obs=1):
'''
Applies a moving average (box) filter on an input time series
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
window_size : float, optional
The size of the moving_average window [days] that will be applied on the
input Series
Default: 1
fillna: bool, optional
Fill nan values at the center window value
min_obs: int
The minimum amount of observations necessary for a valid moving average
Returns
-------
Ser : pandas.Series
moving-average filtered time series
'''
# if index is datetimeindex then convert it to julian date
if type(Ser.index) == pd.DatetimeIndex:
jd_index = julday(np.asarray(Ser.index.month),
np.asarray(Ser.index.day),
np.asarray(Ser.index.year),
np.asarray(Ser.index.hour),
np.asarray(Ser.index.minute),
np.asarray(Ser.index.second))
else:
jd_index = Ser.index.values
filtered = boxcar_filter(
np.atleast_1d(np.squeeze(Ser.values.astype(np.double))),
jd_index.astype(np.double),
window=window_size, fillna=fillna, min_obs=min_obs)
result = pd.Series(filtered, index=Ser.index)
return result
def moving_average(Ser, window_size=1, fillna=False, min_obs=1):
'''
Applies a moving average (box) filter on an input time series
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
window_size : float, optional
The size of the moving_average window [days] that will be applied on the
input Series
Default: 1
fillna: bool, optional
Fill nan values at the center window value
min_obs: int
The minimum amount of observations necessary for a valid moving average
Returns
-------
Ser : pandas.Series
moving-average filtered time series
'''
# if index is datetimeindex then convert it to julian date
if type(Ser.index) == pd.DatetimeIndex:
jd_index = julday(np.asarray(Ser.index.month),
np.asarray(Ser.index.day),
np.asarray(Ser.index.year),
np.asarray(Ser.index.hour),
np.asarray(Ser.index.minute),
np.asarray(Ser.index.second))
else:
jd_index = Ser.index.values
filtered = boxcar_filter(np.atleast_1d(
np.squeeze(Ser.values.astype(np.double))),
jd_index.astype(np.double),
window=window_size,
fillna=fillna,
min_obs=min_obs)
result = pd.Series(filtered, index=Ser.index)
return result
def moving_average(Ser,
window_size=1):
'''
Applies a moving average (box) filter on an input time series
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
window_size : float, optional
The size of the moving_average window [days] that will be applied on the
input Series
Default: 1
Returns
-------
Ser : pandas.Series
moving-average filtered time series
'''
# if index is datetimeindex then convert it to julian date
if type(Ser.index) == pd.DatetimeIndex:
jd_index = julday(np.asarray(Ser.index.month),
np.asarray(Ser.index.day),
np.asarray(Ser.index.year),
np.asarray(Ser.index.hour),
np.asarray(Ser.index.minute),
np.asarray(Ser.index.second))
else:
jd_index = Ser.index.values
filtered = boxcar_filter(
np.atleast_1d(np.squeeze(Ser.values.astype(np.double))),
jd_index.astype(np.double),
window=window_size)
result = pd.Series(filtered, index=Ser.index)
return result
