def test_moving_average_min_observations():
"""
Test moving average filter with datetimeindex.
"""
test_jd = pd.date_range(start='2000-01-01', periods=12, freq='D')
test_data = np.array(
[1, 2, 3, 4, np.nan, np.nan, 8, 9, 10, np.nan, np.nan, np.nan],
dtype=np.double)
ser = pd.Series(test_data, index=test_jd)
filtered = filtering.moving_average(ser,
window_size=5,
fillna=True,
min_obs=3)
np.testing.assert_allclose(
filtered.values,
[2., 2.5, 2.5, 3.0, 5.0, 7.0, 9.0, 9.0, 9.0, np.nan, np.nan, np.nan])
filtered = filtering.moving_average(ser,
window_size=5,
fillna=True,
min_obs=4)
np.testing.assert_allclose(filtered.values, [
np.nan, 2.5, 2.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
np.nan, np.nan, np.nan
])
def calc_climatology(Ser,
moving_avg_orig=5,
moving_avg_clim=30,
median=False,
timespan=None):
'''
Calculates the climatology of a data set
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
moving_avg_orig : float, optional
The size of the moving_average window [days] that will be applied on the
input Series (gap filling, short-term rainfall correction)
Default: 5
moving_avg_clim : float, optional
The size of the moving_average window [days] that will be applied on the
calculated climatology (long-term event correction)
Default: 35
median : boolean, optional
if set to True, the climatology will be based on the median conditions
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
Set this to calculate the climatology based on a subset of the input
Series
Returns
-------
climatology : pandas.Series
Series containing the calculated climatology
'''
if timespan is not None:
Ser = Ser.truncate(before=timespan[0], after=timespan[1])
Ser = moving_average(Ser, window_size=moving_avg_orig)
Ser = pd.DataFrame(Ser)
if type(Ser.index) == pd.DatetimeIndex:
doys = doy(Ser.index.month, Ser.index.day)
else:
year, month, day = julian2date(Ser.index.values)[0:3]
doys = doy(month, day)
Ser['doy'] = doys
if median:
clim = Ser.groupby('doy').median()
else:
clim = Ser.groupby('doy').mean()
return moving_average(pd.Series(clim.values.flatten(), index=clim.index.values), window_size=moving_avg_clim)
def calc_anomaly(Ser,
window_size=35,
climatology=None):
'''
Calculates the anomaly of a time series (Pandas series).
Both, climatology based, or moving-average based anomalies can be
calculated
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex)
window_size : float, optional
The window-size [days] of the moving-average window to calculate the
anomaly reference (only used if climatology is not provided)
Default: 35 (days)
climatology : pandas.Series (index: 1-366), optional
if provided, anomalies will be based on the climatology
timespann : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
If set, only a subset
Returns
-------
anomaly : pandas.Series
Series containing the calculated anomalies
'''
if climatology is not None:
if type(Ser.index) == pd.DatetimeIndex:
doys = doy(Ser.index.month, Ser.index.day)
else:
year, month, day = julian2date(Ser.index.values)[0:3]
doys = doy(month, day)
df = pd.DataFrame()
df['absolute'] = Ser
df['doy'] = doys
clim = pd.DataFrame(climatology, columns=['climatology'])
df = df.join(clim, on='doy', how='left')
anomaly = df['absolute'] - df['climatology']
anomaly.index = df.index
else:
reference = moving_average(Ser, window_size=window_size)
anomaly = Ser - reference
return anomaly
def test_moving_average_size_1():
"""
Test moving average filter with input size 1.
"""
test_jd = np.arange(1, dtype=np.double)
test_data = np.array([1], dtype=np.double)
ser = pd.Series(test_data, index=test_jd)
filtered = filtering.moving_average(ser, window_size=5)
np.testing.assert_allclose(filtered.values, [1.])
def test_moving_average_size_1():
"""
Test moving average filter with input size 1.
"""
test_jd = np.arange(1, dtype=np.double)
test_data = np.array(
[1], dtype=np.double)
ser = pd.Series(test_data, index=test_jd)
filtered = filtering.moving_average(ser, window_size=5)
np.testing.assert_allclose(filtered.values, [1.])
def test_moving_average_dt_index():
"""
Test moving average filter with datetimeindex.
"""
test_jd = pd.date_range(start='2000-01-01', periods=10, freq='D')
test_data = np.array(
[1, 2, 3, 4, -999999.0, 6, 7, 8, 9, np.nan], dtype=np.double)
ser = pd.Series(test_data, index=test_jd)
filtered = filtering.moving_average(ser, window_size=5)
np.testing.assert_allclose(filtered.values, [2., 2.5, 2.5, 3.75, np.nan, 6.25,
7.5, 7.5, 8., np.nan])
def test_moving_average():
"""
Test moving average 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)
ser = pd.Series(test_data, index=test_jd)
filtered = filtering.moving_average(ser, window_size=5)
np.testing.assert_allclose(filtered.values, [2., 2.5, 2.5, 3.75, np.nan, 6.25,
7.5, 7.5, 8., np.nan])
def mask_vod():
dir_in = r'D:\data_sets\AMSR2\timeseries_w_vod'
dir_out = r'D:\data_sets\AMSR2\timeseries'
files = os.listdir(dir_in)
for fname in files:
df = pd.read_csv(os.path.join(dir_in, fname), index_col=0)
df.index = pd.to_datetime(df['vod'].index)
df['vod_ma'] = moving_average(df['vod'], window_size=35)
Ser = df[df['vod_ma'] <= 0.6]['sm']
if len(Ser) > 10:
Ser.to_csv(os.path.join(dir_out, fname))
def calc_climatology(Ser,
moving_avg_orig=5,
moving_avg_clim=30,
median=False,
timespan=None,
fill=np.nan,
wraparound=False):
'''
Calculates the climatology of a data set.
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
moving_avg_orig : float, optional
The size of the moving_average window [days] that will be applied on the
input Series (gap filling, short-term rainfall correction)
Default: 5
moving_avg_clim : float, optional
The size of the moving_average window [days] that will be applied on the
calculated climatology (long-term event correction)
Default: 35
median : boolean, optional
if set to True, the climatology will be based on the median conditions
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
Set this to calculate the climatology based on a subset of the input
Series
fill : float or int, optional
Fill value to use for days on which no climatology exists
wraparound : boolean, optional
If set then the climatology is wrapped around at the edges before
doing the second running average (long-term event correction)
Returns
-------
climatology : pandas.Series
Series containing the calculated climatology
Always has 366 values behaving like a leap year
'''
if timespan is not None:
Ser = Ser.truncate(before=timespan[0], after=timespan[1])
Ser = moving_average(Ser, window_size=moving_avg_orig)
Ser = pd.DataFrame(Ser)
if type(Ser.index) == pd.DatetimeIndex:
doys = doy(Ser.index.month, Ser.index.day)
else:
year, month, day = julian2date(Ser.index.values)[0:3]
doys = doy(month, day)
Ser['doy'] = doys
if median:
clim = Ser.groupby('doy').median()
else:
clim = Ser.groupby('doy').mean()
clim_ser = pd.Series(clim.values.flatten(),
index=clim.index.values)
if wraparound:
index_old = clim_ser.index.copy()
left_mirror = clim_ser.iloc[-moving_avg_clim:]
right_mirror = clim_ser.iloc[:moving_avg_clim]
# Shift index to start at 366 - index at -moving_avg_clim
# to run over a whole year while keeping gaps the same size
right_mirror.index = right_mirror.index + 366 * 2
clim_ser.index = clim_ser.index + 366
clim_ser = pd.concat([left_mirror,
clim_ser,
right_mirror])
clim_ser = moving_average(clim_ser, window_size=moving_avg_clim)
clim_ser = clim_ser.iloc[moving_avg_clim:-moving_avg_clim]
clim_ser.index = index_old
else:
clim_ser = moving_average(clim_ser, window_size=moving_avg_clim)
clim_ser = clim_ser.reindex(np.arange(366) + 1)
clim_ser = clim_ser.fillna(fill)
return clim_ser
def calc_anomaly(Ser,
window_size=35,
climatology=None,
respect_leap_years=True,
return_clim=False):
'''
Calculates the anomaly of a time series (Pandas series).
Both, climatology based, or moving-average based anomalies can be
calculated
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex)
window_size : float, optional
The window-size [days] of the moving-average window to calculate the
anomaly reference (only used if climatology is not provided)
Default: 35 (days)
climatology : pandas.Series (index: 1-366), optional
if provided, anomalies will be based on the climatology
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
If set, only a subset
respect_leap_years : boolean, optional
If set then leap years will be respected during matching of the climatology
to the time series
return_clim : boolean, optional
if set to true the return argument will be a DataFrame which
also contains the climatology time series.
Only has an effect if climatology is used.
Returns
-------
anomaly : pandas.Series
Series containing the calculated anomalies
'''
if climatology is not None:
if type(Ser.index) == pd.DatetimeIndex:
year, month, day = (np.asarray(Ser.index.year),
np.asarray(Ser.index.month),
np.asarray(Ser.index.day))
else:
year, month, day = julian2date(Ser.index.values)[0:3]
if respect_leap_years:
doys = doy(month, day, year)
else:
doys = doy(month, day)
df = pd.DataFrame()
df['absolute'] = Ser
df['doy'] = doys
clim = pd.DataFrame({'climatology': climatology})
df = df.join(clim, on='doy', how='left')
anomaly = df['absolute'] - df['climatology']
anomaly.index = df.index
if return_clim:
anomaly = pd.DataFrame({'anomaly': anomaly})
anomaly['climatology'] = df['climatology']
else:
reference = moving_average(Ser, window_size=window_size)
anomaly = Ser - reference
return anomaly
def calc_climatology(Ser,
moving_avg_orig=5,
moving_avg_clim=30,
median=False,
timespan=None,
fill=np.nan,
wraparound=False,
respect_leap_years=False,
interpolate_leapday=False,
fillna=True,
min_obs_orig=1,
min_obs_clim=1):
'''
Calculates the climatology of a data set.
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
moving_avg_orig : float, optional
The size of the moving_average window [days] that will be applied on the
input Series (gap filling, short-term rainfall correction)
Default: 5
moving_avg_clim : float, optional
The size of the moving_average window [days] that will be applied on the
calculated climatology (long-term event correction)
Default: 35
median : boolean, optional
if set to True, the climatology will be based on the median conditions
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
Set this to calculate the climatology based on a subset of the input
Series
fill : float or int, optional
Fill value to use for days on which no climatology exists
wraparound : boolean, optional
If set then the climatology is wrapped around at the edges before
doing the second running average (long-term event correction)
respect_leap_years : boolean, optional
If set then leap years will be respected during the calculation of
the climatology
Default: False
fillna: boolean, optional
If set, then the moving average used for the calculation of the
climatology will be filled at the nan-values
min_obs_orig: int
Minimum observations required to give a valid output in the first
moving average applied on the input series
min_obs_clim: int
Minimum observations required to give a valid output in the second
moving average applied on the calculated climatology
Returns
-------
climatology : pandas.Series
Series containing the calculated climatology
Always has 366 values behaving like a leap year
'''
if timespan is not None:
Ser = Ser.truncate(before=timespan[0], after=timespan[1])
Ser = moving_average(Ser, window_size=moving_avg_orig, fillna=fillna, min_obs=min_obs_orig)
Ser = pd.DataFrame(Ser)
if type(Ser.index) == pd.DatetimeIndex:
year, month, day = (np.asarray(Ser.index.year),
np.asarray(Ser.index.month),
np.asarray(Ser.index.day))
else:
year, month, day = julian2date(Ser.index.values)[0:3]
if respect_leap_years:
doys = doy(month, day, year)
else:
doys = doy(month, day)
Ser['doy'] = doys
if median:
clim = Ser.groupby('doy').median()
else:
clim = Ser.groupby('doy').mean()
clim_ser = pd.Series(clim.values.flatten(),
index=clim.index.values)
if interpolate_leapday and not respect_leap_years:
clim_ser[60] = np.mean((clim_ser[59], clim_ser[61]))
elif interpolate_leapday and respect_leap_years:
clim_ser[366] = np.mean((clim_ser[365], clim_ser[1]))
if wraparound:
index_old = clim_ser.index.copy()
left_mirror = clim_ser.iloc[-moving_avg_clim:]
right_mirror = clim_ser.iloc[:moving_avg_clim]
# Shift index to start at 366 - index at -moving_avg_clim
# to run over a whole year while keeping gaps the same size
right_mirror.index = right_mirror.index + 366 * 2
clim_ser.index = clim_ser.index + 366
clim_ser = pd.concat([left_mirror,
clim_ser,
right_mirror])
clim_ser = moving_average(clim_ser, window_size=moving_avg_clim, fillna=fillna, min_obs=min_obs_clim)
clim_ser = clim_ser.iloc[moving_avg_clim:-moving_avg_clim]
clim_ser.index = index_old
else:
clim_ser = moving_average(clim_ser, window_size=moving_avg_clim, fillna=fillna, min_obs=min_obs_clim)
clim_ser = clim_ser.reindex(np.arange(366) + 1)
clim_ser = clim_ser.fillna(fill)
return clim_ser
def calc_anomaly(Ser,
window_size=35,
climatology=None,
respect_leap_years=True,
return_clim=False):
'''
Calculates the anomaly of a time series (Pandas series).
Both, climatology based, or moving-average based anomalies can be
calculated
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex)
window_size : float, optional
The window-size [days] of the moving-average window to calculate the
anomaly reference (only used if climatology is not provided)
Default: 35 (days)
climatology : pandas.Series (index: 1-366), optional
if provided, anomalies will be based on the climatology
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
If set, only a subset
respect_leap_years : boolean, optional
If set then leap years will be respected during matching of the climatology
to the time series
return_clim : boolean, optional
if set to true the return argument will be a DataFrame which
also contains the climatology time series.
Only has an effect if climatology is used.
Returns
-------
anomaly : pandas.Series
Series containing the calculated anomalies
'''
if climatology is not None:
if type(Ser.index) == pd.DatetimeIndex:
year, month, day = (np.asarray(Ser.index.year),
np.asarray(Ser.index.month),
np.asarray(Ser.index.day))
else:
year, month, day = julian2date(Ser.index.values)[0:3]
if respect_leap_years:
doys = doy(month, day, year)
else:
doys = doy(month, day)
df = pd.DataFrame()
df['absolute'] = Ser
df['doy'] = doys
clim = pd.DataFrame({'climatology': climatology})
df = df.join(clim, on='doy', how='left')
anomaly = df['absolute'] - df['climatology']
anomaly.index = df.index
if return_clim:
anomaly = pd.DataFrame({'anomaly': anomaly})
anomaly['climatology'] = df['climatology']
else:
reference = moving_average(Ser, window_size=window_size)
anomaly = Ser - reference
return anomaly
def calc_climatology(Ser,
moving_avg_orig=5,
moving_avg_clim=35,
moving_avg_month_clim=3,
median=False,
timespan=None,
fill=np.nan,
wraparound=True,
respect_leap_years=False,
interpolate_leapday=False,
fillna=True,
min_obs_orig=1,
min_obs_clim=1,
unit="day"):
"""
Calculates the climatology of a data set.
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
moving_avg_orig : float, optional
The size of the moving_average window [days] that will be applied on the
input Series (gap filling, short-term rainfall correction)
Default: 5
moving_avg_clim : float, optional
The size of the moving_average window in days that will be applied on the
calculated climatology (long-term event correction)
Default: 35
moving_avg_month_clim: : float, optional
Same as for 'moving_avg_clim', but applied to monthly climatologies. In case
unit='month', this value overrides 'moving_avg_clim'
Default: 3
median : boolean, optional
if set to True, the climatology will be based on the median conditions
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
Set this to calculate the climatology based on a subset of the input
Series
fill : float or int, optional
Fill value to use for days on which no climatology exists
wraparound : boolean, optional
If set then the climatology is wrapped around at the edges before
doing the second running average (long-term event correction)
respect_leap_years : boolean, optional
If set then leap years will be respected during the calculation of
the climatology. Only valid with 'unit' value set to 'day'.
Default: False
interpolate_leapday: boolean, optional
<description>. Only valid with 'unit' value set to 'day'.
Default: False
fillna: boolean, optional
If set, then the moving average used for the calculation of the
climatology will be filled at the nan-values
min_obs_orig: int
Minimum observations required to give a valid output in the first
moving average applied on the input series
min_obs_clim: int
Minimum observations required to give a valid output in the second
moving average applied on the calculated climatology
unit: str, optional
Unit of the year to apply the climatology calculation to. Currently,
supported options are 'day', 'month'.
Default: 'day'
Returns
-------
climatology : pandas.Series
Series containing the calculated climatology
Always has 366 values behaving like a leap year
"""
if unit != "day":
respect_leap_years, interpolate_leapday = False, False
if unit == "month":
moving_avg_clim = moving_avg_month_clim
if timespan is not None:
Ser = Ser.truncate(before=timespan[0], after=timespan[1])
Ser = moving_average(Ser,
window_size=moving_avg_orig,
fillna=fillna,
min_obs=min_obs_orig)
Ser = pd.DataFrame(Ser)
if type(Ser.index) == pd.DatetimeIndex:
year, month, day = (np.asarray(Ser.index.year),
np.asarray(Ser.index.month),
np.asarray(Ser.index.day))
else:
year, month, day = julian2date(Ser.index.values)[0:3]
# provide indices for the selected unit
indices, n_idx = _index_units(year,
month,
day,
unit=unit,
respect_leap_years=respect_leap_years)
Ser['unit'] = indices
if median:
clim = Ser.groupby('unit').median()
else:
clim = Ser.groupby('unit').mean()
clim_ser = pd.Series(clim.values.flatten(), index=clim.index.values)
clim_ser = clim_ser.reindex(np.arange(n_idx) + 1)
if wraparound:
index_old = clim_ser.index.copy()
left_mirror = clim_ser.iloc[-moving_avg_clim:]
right_mirror = clim_ser.iloc[:moving_avg_clim]
# Shift index to start at n_idx - index at -moving_avg_clim
# to run over a whole year while keeping gaps the same size
right_mirror.index = right_mirror.index + n_idx * 2
clim_ser.index = clim_ser.index + n_idx
clim_ser = pd.concat([left_mirror, clim_ser, right_mirror])
clim_ser = moving_average(clim_ser,
window_size=moving_avg_clim,
fillna=fillna,
min_obs=min_obs_clim)
clim_ser = clim_ser.iloc[moving_avg_clim:-moving_avg_clim]
clim_ser.index = index_old
else:
clim_ser = moving_average(clim_ser,
window_size=moving_avg_clim,
fillna=fillna,
min_obs=min_obs_clim)
# keep hardcoding as it's only for doys
if interpolate_leapday and not respect_leap_years:
clim_ser[60] = np.mean((clim_ser[59], clim_ser[61]))
elif interpolate_leapday and respect_leap_years:
clim_ser[366] = np.mean((clim_ser[365], clim_ser[1]))
clim_ser = clim_ser.fillna(fill)
return clim_ser
