tsprocess — Time series processing

This module provides some helper functions for time series processing such as calculation of Evaporation, Bioclimatic Indeces time series etc

Helper functions

tsprocess.GetTimeseriesCommonPeriod(tslist[, start_date=None][, end_date=None][, interval_exclusive=False][, reject_nulls=True])

A function inspired from delphi version of thelma.tsprocess.pas GetCommonPeriod. Reads the time series contained in the list object tslist and returns a list of datetime objects representing their common period. By default the common period would not contain time stamp of time series with null values. To override this set the reject_nulls paremeter to True. By specifying start_date and / or end_date it returns a common period bounded by the interval defined by these two parameters respectively. Set interval_exclusive if you want the above interval to be exclusive for dates or else it is inclusive by default.

tsprocess.MultiTimeseriesProcess(method, timeseries_arg, out_timeseries[, options={}])

Execute processing of multi time series, e.g. calculates the heat index time series by airtemp and relhum timeseries. method is a string with possible values of:

• HeatIndex
• SSI
• IDM_monthly
• IDM_annual
• BaromFormula
• OneStepDiff

timeseries_arg is a dictionary containg the input time series. For HeatIndex and SSI processing it should contain two time series specified by the keys 'temp' and 'rh' (e.g. {'temp': temp_ts, 'rh': rh_ts}). For IDM a total (monthly or annual respect.) preciptiation should be specified with the 'precip' key. For Barometric Formula , method name is BaromFormula, ‘temp’ is temperature time series (in deg C) and ‘press’ the atmospheric pressure (in hPa), you have also to pass and altitude difference in options as options[‘hdiff’]. out_timeseries should be a Timeseries object to write results. options is a dictionary holding the options. ‘start_date’ and ‘end_date’ in options could contain the bounds for the process. These dates are being passed in GetTimeseriesCommonPeriod(). When null values are passed, the output in these dates is also null. For OneStepDiff method, the time step of timeseries should be strict (it should have time_step defined) or else an exception is raised.

tsprocess.MultiTimeseriesProcessDb(method, timeseries_arg, out_timeseries_id, db, read_tstep_func[, transaction=None][, commit=True][, options={}])

Execute processing of multi time series by using the MultiTimeseriesProcess() function. For method see MultiTimeseriesProcess. timeseries_arg is a dictionary holding source time series ids as values, for keys values and their meaning see MultiTimeseriesProcess. out_timeseries_id is the id of the time series that will be written in the database db. You may also set a database transaction or set the commit control. Add a key ‘append_only’ in options with a value of True if you want to just append new values in destination time series. In the case of append, the function will try to obtain data from source time series bottom fields to gain performance. read_tstep_func should be a function taking the argument of time series id.

tsprocess.AggregateDbTimeseries(source_id, dest_id, db, read_tstep_func[, transaction=None][, commit=None][, missing_allowed=0.0][, missing_flag='MISSING'][, append_only=False][, last_incomplete=False][, all_incomplete=False])

Do time series aggregation. For details on aggregation algorithm see the help of pthelma.timeseries.aggregate(). source_id and dest_id are the database ids of source and destination time series respectively. db is database object. read_tstep_func should be a function taking the argument of time series id. For other options see aggregate and timeseries.write_to_db. If append_only is set to True, the writing process will execute with the timeseries.append_to_db by writing only the new records. Currently the AggregateDbTimeseries will read the full source time series from database. In a future version, to improve performace, it should try to obtain records from time series bottom field.

tsprocess.InterpolateDbTimeseries(source_id, dest_id, curve_type, curve_data, db[, data_columns=(0, 1)][, logarithmic=False][, offset=0][, append_only=False][, transaction=None][, commit=True])

Interpolate the time series with source_id in the curve described by curve_data and then write the results to a database time series with id of dest_id. curve_data is a string containing the curve(s) definition according to pthelma.curves specifications. curve_type is a string that should provided and takes one of the values 'SingleCurve' or 'StageDischargeMulti'. data_columns, logarithmic and offset have meaning only if ‘SingleCurve’ is considered. For data_columns, logarithmic see pthelma.curves documentation. offset is the offset for independent variable, used only if logarithmic set to True. You may also set a database transaction or set the commit control. Set append_only to True if you want to just append new values in destination time series. In the case of append, the function will try to obtain data from source time series bottom fields to gain performance.