pytesmo.time_series package

Submodules

pytesmo.time_series.anomaly module

Created on June 20, 2013

pytesmo.time_series.anomaly.calc_anomaly(Ser, window_size=35, climatology=None, respect_leap_years=True, return_clim=False)[source]

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 – Series containing the calculated anomalies

Return type:

pandas.Series

pytesmo.time_series.anomaly.calc_climatology(Ser, moving_avg_orig=5, moving_avg_clim=30, median=False, timespan=None, fill=nan, wraparound=False)[source]

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 – Series containing the calculated climatology Always has 366 values behaving like a leap year

Return type:

pandas.Series

pytesmo.time_series.filtering module

Created on Oct 16, 2013

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

pytesmo.time_series.filtering.moving_average(Ser, window_size=1)[source]

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 – moving-average filtered time series

Return type:

pandas.Series

pytesmo.time_series.filters module

Created on Oct 16, 2013

Fast cython functions for calculating various filters

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

pytesmo.time_series.filters.boxcar_filter(ndarray in_data, ndarray in_jd, float window=1, double nan=-999999.0)

Calculates filtered time series using a boxcar filter - basically a moving average calculation

Parameters:
  • in_data (double numpy.array) – input data
  • in_jd (double numpy.array) – julian dates of input data
  • window (int) – characteristic time used for calculating the weight
  • nan (double) – nan values to exclude from calculation
pytesmo.time_series.filters.exp_filter(ndarray in_data, ndarray in_jd, int ctime=10, double nan=-999999.0)

Calculates exponentially smoothed time series using an iterative algorithm

Parameters:
  • in_data (double numpy.array) – input data
  • in_jd (double numpy.array) – julian dates of input data
  • ctime (int) – characteristic time used for calculating the weight
  • nan (double) – nan values to exclude from calculation

pytesmo.time_series.grouping module

Module provides grouping functions that can be used together with pandas to create a few strange timegroupings like e.g. decadal products were there are three products per month with timestamps on the 10th 20th and last of the month

pytesmo.time_series.grouping.group_by_day_bin(df, bins=[1, 11, 21, 32], start=False, dtindex=None)[source]

Calculates timegroups for a given daterange. Groups are from day 1-10, 11-20, 21-last day of each month.

Parameters:
  • df (pandas.DataFrame) – DataFrame with DateTimeIndex for which the grouping should be done
  • bins (list, optional) – bins in day of the month, default is for dekadal grouping
  • start (boolean, optional) – if set to True the start of the bin will be the timestamp for each observations
  • dtindex (pandas.DatetimeIndex, optional) – precomputed DatetimeIndex that should be used for resulting groups, useful for processing of numerous datasets since it does not have to be computed for every call
Returns:

  • grouped (pandas.core.groupby.DataFrameGroupBy) – DataFrame groupby object according the the day bins on this object functions like sum() or mean() can be called to get the desired aggregation.
  • dtindex (pandas.DatetimeIndex) – returned so that it can be reused if possible

pytesmo.time_series.grouping.grouped_dates_between(start_date, end_date, bins=[1, 11, 21, 32], start=False)[source]

Between a start and end date give all dates that represent a bin See test for example.

Parameters:
  • start_date (date) – start date
  • end_date (date) – end date
  • bins (list, optional) – bin start values as days in a month e.g. [0,11,21] would be two bins one with values 0<=x<11 and the second one with 11<=x<21
  • start (boolean, optional) – if True the start of the bins is the representative date
Returns:

tstamps – list of representative dates between start and end date

Return type:

list of datetimes

pytesmo.time_series.grouping.grp_to_datetimeindex(grps, bins, dtindex, start=False)[source]

Makes a datetimeindex that has for each entry the timestamp of the bin beginning or end this entry belongs to.

Parameters:
  • grps (numpy.array) – group numbers made by np.digitize(data, bins)
  • bins (list) – bin start values e.g. [0,11,21] would be two bins one with values 0<=x<11 and the second one with 11<=x<21
  • dtindex (pandas.DatetimeIndex) – same length as grps, gives the basis datetime for each group
  • start (boolean, optional) – if set to True the start of the bin will be the timestamp for each observations
Returns:

grpdt – Datetimeindex where every date is the end of the bin the datetime ind the input dtindex belongs to

Return type:

pd.DatetimeIndex

pytesmo.time_series.plotting module

Created on Mar 7, 2014

Plot anomalies around climatology using colors

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

pytesmo.time_series.plotting.plot_clim_anom(df, clim=None, axes=None, markersize=0.75, mfc='0.3', mec='0.3', clim_color='0.0', clim_linewidth=0.5, clim_linestyle='-', pos_anom_color='#799ADA', neg_anom_color='#FD8086', anom_linewidth=0.2, add_titles=True)[source]

Takes a pandas DataFrame and calculates the climatology and anomaly and plots them in a nice way for each column

Parameters:
  • df (pandas.DataFrame) –
  • clim (pandas.DataFrame, optional) – if given these climatologies will be used if not given then climatologies will be calculated this DataFrame must have the same number of columns as df and also the column names. each climatology must have doy as index.
  • axes (list of matplotlib.Axes, optional) – list of axes on which each column should be plotted if not given a standard layout is generated
  • markersize (float, optional) – size of the markers for the datapoints
  • mfc (matplotlib color, optional) – markerfacecolor, color of the marker face
  • mec (matplotlib color, optional) – markeredgecolor
  • clim_color (matplotlib color, optional) – color of the climatology
  • clim_linewidth (float, optional) – linewidth of the climatology
  • clim_linestyle (string, optional) – linestyle of the climatology
  • pos_anom_color (matplotlib color, optional) – color of the positive anomaly
  • neg_anom_color (matplotlib color, optional) – color of the negative anomaly
  • anom_linewidth (float, optional) – linewidth of the anomaly lines
  • add_titles (boolean, optional) – if set each subplot will have it’s column name as title Default : True
Returns:

  • Figure (matplotlib.Figure) – if no axes were given
  • axes (list of matploblib.Axes) – if no axes were given

Module contents