def test_julian2date(): """ Test julian2date. """ year, month, day, hour, minute, second, ms = julian2date( 2457533.9306828701) assert type(year) == int assert year == 2016 assert month == 5 assert day == 25 assert hour == 10 assert minute == 20 assert second == 10 assert ms == 999976 year, month, day, hour, minute, second, ms = julian2date(2454515.40972) assert year == 2008 assert month == 2 assert day == 18 assert hour == 21 assert minute == 49 assert second == 59 assert ms == 807989
def test_julian2date_array(): """ Test julian2date. """ year, month, day, hour, minute, second, micro = julian2date( np.array([2457533.9306828701, 2457533.9306828701])) nptest.assert_almost_equal(year, np.array([2016, 2016])) nptest.assert_almost_equal(month, np.array([5, 5])) nptest.assert_almost_equal(day, np.array([25, 25])) nptest.assert_almost_equal(hour, np.array([10, 10])) nptest.assert_almost_equal(minute, np.array([20, 20])) nptest.assert_almost_equal(second, np.array([10, 10])) nptest.assert_almost_equal(micro, np.array([999976, 999976]))
def test_julian2date_single_array(): """ Test julian2date single array. """ year, month, day, hour, minute, second, micro = julian2date( np.array([2457533.9306828701])) assert type(year) == np.ndarray assert year == 2016 assert month == 5 assert day == 25 assert hour == 10 assert minute == 20 assert second == 10 assert micro == 999976
def julian2doy(j, consider_nonleap_years=True): """ Calendar date from julian date. Works only for years past 1582! Parameters ---------- j : numpy.ndarray or double Julian days. consider_nonleap_years : boolean, optional Flag if all dates are interpreted as leap years (False) or not (True). Returns ------- doy : numpy.ndarray or int32 Day of year. """ return julian2date(j, return_doy=True, doy_leap_year=not consider_nonleap_years)[-1]
def julian2datetimeindex(j, tz=pytz.UTC): """ Converting Julian days to datetimeindex. Parameters ---------- j : numpy.ndarray or int32 Julian days. tz : instance of pytz, optional Time zone. Default: UTC Returns ------- datetime : pandas.DatetimeIndex Datetime index. """ year, month, day, hour, minute, second, microsecond = julian2date(j) return pd.DatetimeIndex([datetime(y, m, d, h, mi, s, ms, tz) for y, m, d, h, mi, s, ms in zip(year, month, day, hour, minute, second, microsecond)])
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 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) 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
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