.. algorithm:: .. summary:: .. relatedalgorithms:: .. properties:: Description ----------- The values used to define the fitting spline must be in the sample log ``LogToInterpolate``. The interpolated values will be fit to the time-series axis in ``LogToMatch``. If the time range in ``LogToMatch`` is outside the range of ``LogToInterpolate``, the time axis will simply be truncated to fit, without extrapolation. Usage ----- **Example: Interpolating a sample log** .. testcode:: AddLogInterpolated ws = CreateSampleWorkspace() # add the time series to interpolate AddTimeSeriesLog(ws,"interp","2010-01-01T00:00:00",1.0,DeleteExisting=False) AddTimeSeriesLog(ws,"interp","2010-01-01T00:00:10",2.0,DeleteExisting=False) AddTimeSeriesLog(ws,"interp","2010-01-01T00:00:20",6.0,DeleteExisting=False) AddTimeSeriesLog(ws,"interp","2010-01-01T00:00:30",4.0,DeleteExisting=False) # add the time values to match against AddTimeSeriesLog(ws,"match","2010-01-01T00:00:03",0.0,DeleteExisting=False) AddTimeSeriesLog(ws,"match","2010-01-01T00:00:07",0.0,DeleteExisting=False) AddTimeSeriesLog(ws,"match","2010-01-01T00:00:11",0.0,DeleteExisting=False) AddTimeSeriesLog(ws,"match","2010-01-01T00:00:15",0.0,DeleteExisting=False) AddTimeSeriesLog(ws,"match","2010-01-01T00:00:16",0.0,DeleteExisting=False) AddTimeSeriesLog(ws,"match","2010-01-01T00:00:21",0.0,DeleteExisting=False) AddLogInterpolated(ws, "interp", "match") for logName in ["interp","interp_interpolated"]: print("Log: {}".format(logName)) print(ws.getRun().getProperty(logName).valueAsString()) Output: .. testoutput:: AddLogInterpolated :options: +NORMALIZE_WHITESPACE Log: interp 2010-Jan-01 00:00:00 1 2010-Jan-01 00:00:10 2 2010-Jan-01 00:00:20 6 2010-Jan-01 00:00:30 4 Log: interp_interpolated 2010-Jan-01 00:00:03 0.9724 2010-Jan-01 00:00:07 1.2716 2010-Jan-01 00:00:11 2.373 2010-Jan-01 00:00:15 4.225 2010-Jan-01 00:00:16 4.688 2010-Jan-01 00:00:21 6.1078 .. categories:: .. sourcelink::