TSTool-Vol2-CommandReference - Laserfiche WebLink

value is needed. OutputColumn The name of a column to receive the output. None – must be specified. 240 Command Reference: Multiply() Multiply the data values in a time series by data values in another time series Version 08.16.04, 2008-09-24 The Multiply()command multiplies one time series by another. Missing data in either time series causes the result to be missing. See also the Scale() command, which multiplies time series by a numerical value. The following dialog is used to edit the command and illustrates the syntax of the command. Multiply Multiply() Command Editor 241 Command Reference – Multiply() -1 Multiply() Command TSTool Documentation The command syntax is as follows: Multiply(Parameter=Value,…) Command Parameters Parameter Description Default TSID The time series identifier or alias for the time series to be modified. None – must be specified. MultiplierTSID The time series identifier or alias for the time series that is the multiplier. None – must be specified. A sample command file is as follows (this example does not necessarily make sense but illustrates how the Multiply() command can be used for numerical calculations in an analysis): # 2184 -DEL NORTE 2 E 2184.NOAA.TempMean.Month~HydroBase # 5706 -MONTE VISTA 2 W 5706.NOAA.TempMean.Month~HydroBase Multiply(TSID="2184.NOAA.TempMean.Month", MultiplierTSID="5706.NOAA.TempMean.Month") Command Reference – Multiply() -2 242 Command Reference: NewDayTSFromMonthAndDayTS() Create a new daily time series from monthly total and daily pattern Version 10.00.01, 2011-05-12 The NewDayTSFromMonthAndDayTS() command creates a new daily time series by distributing a monthly time series “volume” according to the pattern of the independent daily time series. This command currently only handles processing monthly ACFT and daily CFS time series. This command is useful where a monthly flow time series is known at a location, and a daily pattern is known at a related gage. The new time series is assigned the given identifier and alias. The following calculations are performed: ⎟ ⎟ ⎟ ⎟ ⎠⎞ ⎜ ⎜ ⎜ ⎜ ⎝⎛ ∗ ⎟ ⎟⎠ ⎞ ⎜ ⎜⎝ ⎛ ⎟⎠⎞ ⎜⎝= ∗ ⎛ Σ = = i Nday Month i i i i DayTS DayTS ACFT s DAY NDAYS DayTS MonthTS ACFT sin1 2 1 1 1 43560 86400 2 2 1 where, for days in a month: DayTS2i = the daily value being estimated in daily time series 2 MonthTS2 = the monthly value being used for volumes for time series 2, shown in units of ACFT/NDAYS (equivalent to ACFT/Month) NDAYS = the number of days in the month DayTS1i = the daily value for indicator daily time series 1 ΣDayTS1i = the sum of the daily values for indicator time series for the a month In summary, the monthly volume in ACFT/NDAYS is first converted to an average monthly CFS rate by multiplying by 43560/86400 (or 1/1.9835), and finally the average CFS value is prorated by the ratio of the indicator daily time series daily value divided by the total daily flows for the month, to give a daily CFS value for each day of the month. In this case, the last term is simply a ratio (converting daily average CFS to daily ACFT and calculating the ratio would result in the same value). Days with missing data are excluded from the summation and the estimated values. The output period is the global output period from SetOutputPeriod(), or if not set the period from the daily time series is used. 24 3 Command Reference – NewDayTSFromMonthAndDayTS() -1 NewDayTSFromMonthAndDayTS() Command TSTool Documentation For example, consider May a may total for MonthTS2 = 1001.7 ACFT and daily values (CFS) as follows: Day 1 = 14 14 13 13 14 14 15 15 15 16 17 17 16 18 18 17 18 18 18 18 17 17 17 17 16 16 17 18 18 17 Day 31 = 17 The total is 505 CFS. The estimated value for day 1 of the second daily time series would then be: 1001.7 * (1/1.9835) * (14/505) = 14 CFS In this case, the indicator time series was the same as the time series being estimated and therefore the estimated value should be the same as the indicator. Command Reference – NewDayTSFromMonthAndDayTS() -2 244 TSTool Documentation NewDayTSFromMonthAndDayTS() Command The following dialog is used to edit the command and illustrates the syntax for the command. NewDayTSFromMonthAndDayTS_Alias NewDayTSFromMonthAndDayTS() Command Editor The command syntax is as follows: NewDayTSFromMonthAndDayTS(Parameter=Value,…) The following older command syntax is updated to the above syntax when a command file is read: TS Alias = NewDayTSFromMonthAndDayTS(Parameter=Value,…) Command Parameters Parameter Description Default MonthTSID The time series identifier or alias for a monthly time series supplying monthly ACFT values. None – must be specified. DayTSID The time series identifier or alias for a daily time series supplying daily flow values (only the pattern is used). None – must be specified. NewTSID The time series identifier of the new time series. The interval must be Day. None – must be specified. Alias The alias to assign to the time series, as a literal string or using the special formatting characters listed by the command editor. The alias is a short identifier used by other commands to locate time series for processing, as an alternative to the time series identifier (TSID). None – must be specified. 24 5 Command Reference – NewDayTSFromMonthAndDayTS() -3 NewDayTSFromMonthAndDayTS() Command TSTool Documentation A sample command file to process data from the State of Colorado’s HydroBase is as follows: SetOutputPeriod(OutputStart="1950-01",OutputEnd="2 002-12") # 08236500 -ALAMOSA RIVER BELOW TERRACE RESERVOIR 08236500.DWR.Streamflow.Day~HydroBase # 08236000 -ALAMOSA RIVER ABOVE TERRACE RESERVOIR 08236000.DWR.Streamflow.Month~HydroBase NewDayTSFromMonthAndDayTS( NewTSID="08236000.DWR.Streamflow.Day", MonthTSID="08236000.DWR.Streamflow.Month", DayTSID="08236500.DWR.Streamflow.Day",Alias=”DayTS”) A graph of data resulting from this command may look similar to the following. Note that the each time series has a similar pattern, but at different levels. NewDayTSFromMonthAndDayTS_Graph Result of NewDayTSFromMonthAndDayTS () Command Command Reference – NewDayTSFromMonthAndDayTS() -4 246 Command Reference: NewEndOfMonthTSFromDayTS() Use a daily time series to create an end of month time series Version 10.00.00, 2011-03-27 The NewEndOfMonthTSFromDayTS() command is typically used to convert a daily reservoir storage time series to an end of month reservoir storage time series. The command can also be applied to other data types (e.g., measured well levels). Changing from a daily to an end of month monthly time series is accomplished by starting on the month ending day and searching in both directions (backward then forward by expanding until the bracket is reached) for a daily measurement. The number of days to search in each direction (the bracket) should not be so large as to produce unrealistic results. It is possible that no value will be found for a particular month, with the given restraints. In this case, other fill commands (e.g., FillInterpolate()) can be applied to estimate the remaining missing data. The following dialog is used to edit the command and illustrates the syntax of the command. NewEndOfMonthTSFromDayTS_Alias NewEndOfMonthTSFromDayTS() Command Editor 24 7 Command Reference – NewEndOfMonthTSFromDayTS() -1 NewEndOfMonthTSFromDayTS() Command TSTool Documentation The command syntax is as follows: NewEndOfMonthTSFromDayTS(Parameter=Value,…) The following older command syntax is updated to the above syntax when a command file is read: TS Alias = NewEndOfMonthTSFromDayTS (Parameter=Value,…) Command Parameters Parameter Description Default DayTSID The time series identifier or alias of the daily time series to be searched for data. None – must be specified. Alias The alias to assign to the time series, as a literal string or using the special formatting characters listed by the command editor. The alias is a short identifier used by other commands to locate time series for processing, as an alternative to the time series identifier (TSID). None – must be specified. Bracket The number of days to search from the end of the month, in order to find a daily value to transfer to the end of the month. None – must be specified. A sample command file for estimating reservoir contents, using data from the State of Colorado’s HydroBase database is: # 2003536 -CONTINENTAL RES 2003536.DWR.ResMeasStorage.Day~HydroBase NewEndOfMonthTSFromDayTS(Alias=”Continental”, DayTSID="2003536.DWR.ResMeasStorage.Day",Bracket=15) A sample command file for estimating well levels is: # 384549104445101 -SCO1506611ABC 384549104445101.USGS.WellLevel.Day~HydroBase NewEndOfMonthTSFromDayTS(Alias=”WellMonth”, DayTSID="384549104445101.USGS.WellLevel.Day",Bracket=30) FillInterpolate(TSList=AllMatchingTSID,TSID="WellMont h", MaxIntervals=0,Transformation=None) Command Reference – NewEndOfMonthTSFromDayTS() -2 248 TSTool Documentation NewEndOfMonthTSFromDayTS() Command To evaluate the results of this command, it is useful to graph both the input and results, changing the graph properties to add symbols to see the individual measurements, as shown in the following figure. NewEndOfMonthTSFromDayTS_Graph Results of NewEndOfMonthTSFromDayTS() Command 24 9 Command Reference – NewEndOfMonthTSFrom DayTS() -3 NewEndOfMonthTSFromDayTS() Command TSTool Documentation This page is intentionally blank. Command Reference – NewEndOfMonthTSFromDayTS() -4 250 Command Reference – NewEnsemble () -1 Command Reference: NewEnsemble () Create a new ensemble and optionally include 1+ time series Version 10.11.00, 2012-07-18 The NewEnsemble() command creates a new ensemble and optionally inserts 1+ existing time series. For example, use the command to create an ensemble that includes multiple scenarios. Although it is typical that an ensemble contains time series at the same location, it is also possible to use ensembles to group time series at different locations (e.g., to group all time series for stations in a county). It is envisioned that time series added to the ensemble can optionally be copied and the period changed, in order to isolate the data from the original time series. However, currently the time series from the main processor list are simply associated with the ensemble. Consequently, if other commands change the time series (for example free the time series), the ensemble will reflect the changes. Overcoming this issue will require design changes that need to be evaluated. The following dialog is used to edit the command and illustrates the syntax for the command. NewEnsemble NewEnsemble () Command Editor The command syntax is as follows: NewEnsemble (Parameter=Value,…) Command Parameters Parameter Description Default TSList Indicates the list of time series to be processed, one of: AllTS 251 NewEnsemble () Command TSTool Documentation Command Reference – NewEnsemble() -2 Parameter Description Default • AllMatchingTSID – all time series that match the TSID (single TSID or TSID with wildcards). • AllTS – all time series before the command. • EnsembleID – all time series in the ensemble. • FirstMatchingTSID – the first time series that matches the TSID (single TSID or TSID with wildcards). • LastMatchingTSID – the last time series that matches the TSID (single TSID or TSID with wildcards). • SelectedTS – the time series are those selected with the SelectTimeSeries() command. TSID The time series identifier or alias for the time series to be processed, using the * wildcard character to match multiple time series. Required when TSList=*TSID EnsembleID The ensemble to be processed, if processing an ensemble. Required when TSList=EnsembleID. NewEnsembleID The new ensemble identifier. None – must be specified. NewEnsembleName The name for the new ensemble. Blank. InputStart The date/time to start transferring data from the time series. Envisioned as future enhancement. Use all data. InputEnd The date/time to end transferring data from the time series. Envisioned as future enhancement. Use all data. CopyTimeSeries Copy the time series to the ensemble rather than using time series in the main time series list. This protects the data in the ensemble from general processing commands. Envisioned as future enhancement. Associate time series in the main time series list with the new ensemble. A sample command file to create an ensemble from user-defined time series is as follows: # Test creating an ensemble from year interval time series NewPatternTimeSeries(Alias=”ts1”,NewTSID="ts1..Flow.Year", SetStart="1960",SetEnd="2000",Units="ACFT", PatternValues="1,2,5,8,,20") NewPatternTimeSeries(Alias=”ts2”,NewTSID="ts2..Flow.Year", SetStart="1950",SetEnd="2005",Units="ACFT", PatternValues="2,4,10,16,,40") NewEnsemble(TSList=AllTS, NewEnsembleID="TestEnsemble",NewEnsembleName="Test Ensemble") 252 Command Reference – NewPatternTimeSeries() -1 Command Reference: NewPatternTimeSeries() Create a new time series containing a pattern of repeating values Version 10.06.00, 2012-04-01 The NewPatternTimeSeries() command creates a new time series containing a repeating pattern of numbers. This command is useful for generating data to test other commands. The following dialog is used to edit the command and illustrates the syntax for the command. NewPatternTimeSeries NewPatternTimeSeries() Command Editor The command syntax is as follows: NewPatternTimeSeries(Parame ter=Value,…) The following older command syntax is updated to the above syntax when a command file is read: TS Alias = NewPatternTimeSeries(Parameter=Value,…) 253 NewPatternTimeSeries() Command TSTool Documentation Command Reference – NewPatternTimeSeries() -2 Command Parameters Parameter Description Default Alias The alias to assign to the time series, as a literal string or using the special formatting characters listed by the command editor. The alias is a short identifier used by other commands to locate time series for processing, as an alternative to the time series identifier (TSID). None – must be specified. NewTSID The time series identifier to be assigned to the new time series, which is useful to avoid confusion with the original time series. None – must be specified. IrregularInterval Interval to use to populate irregular time series (e.g., 1Hour, Month), necessary because data need to be assigned somehow. None – must be specified for irregular time series. Description Description for the time series. None. SetStart Start date/time to set data. None – must be specified. SetEnd End date/time to set data. None – must be specified. Units Units for the data values. None. MissingValue Value to use to indicate missing data values. -999 is the default for historical reasons; however, NaN (not a number) is being phased in and should be specified if possible. Time series can be missing and be flagged. -999 PatternValues Data values, separated by commas. Missing values can be omitted (e.g., indicate with adjacent commas). None – must be specified. PatternFlags Short strings to assign to the values (used to annotate graphs and other output) separated by commas. Missing flags can be omitted (e.g., indicate with adjacent commas). No flags are assigned. Examples The following example command file illustrates how to create a pattern time series for testing: NewPatternTimeSeries(Alias=”ts1”,NewTSID="ts1..Streamflow.Day", Description="Test data",SetStart="1950-01-01", SetEnd="1951-03-12",Un its="CFS",PatternValues="5,10,12,13,75") WriteDateValue(OutputFile=",Example_NewPatternTimeSeries_out.dv") 254 Command Reference – NewStatisticTimeSeries() -1 Command Reference: NewStatisticTimeSeries() Create a time series containing a repeating year of statistics determined from a time series Version 10.11.00, 2012-07-03 The NewStatisticTimeSeries() command uses data from a time series to calculate a statistic for each interval in the year, and assigns the statistic value to each corresponding interval for the full period. For example, for a statistic of Mean calculated from a daily time series, all January 1 values are averaged and the resulting January 1 values for the entire time series are set to the mean value. Similarly, if monthly data are analyzed, all January values in the result will be set to the mean of the January values in the original time series. This command is useful for superimposing the long-term historical statistic on the original time series or real-time conditions. Leap year statistics are computed from Feb 29 values and are visible only in leap years of the output time series. Missing data in the original time series will by default still result in the statistic being computed, but the AllowMissingCount and MinimumSampleSize parameters control the impacts of missing values. The following dialog is used to edit the command and illustrates the syntax for the command. NewStatisticTimeSeries NewStatisticTimeSeries() Command Editor 255 NewStatisticTimeSeries() Command TSTool Documentation Command Reference – NewStatisticTimeSeries() -2 The command syntax is as follows: NewStatisticTimeSeries(Parameter=value,…) The following older command syntax is updated to the above syntax when a command file is read: TS Alias = NewStatisticTimeSeries(Parameter=value,…) Command Parameters Parameter Description Default TSID The time series identifier (or alias) of the time series to analyze. None – must be specified. Alias The alias to assign to the time series, as a literal string or using the special formatting characters listed by the command editor. The alias is a short identifier used by other commands to locate time series for processing, as an alternative to the time series identifier (TSID). None – must be specified. NewTSID The time series identifier to be assigned to the new time series, which is needed to avoid confusion with the original time series. Use the same identifier as the original time series with the statistic appended to the scenario. Statistic See the Available Statistics table below. None – must be specified. Allow Missing Count The number of missing values allowed in the source interval(s) in order to produce a result. This capability should be used with care because it may result in data that are not representative of actual conditions. Allow any number of missing values. MinimumSampleSize The minimum number of values required in the sample to compute the statistic. If the minimum sample size is not available, the result will be set to missing. Minimum sample size is defined by the statistic. AnalysisStart The date/time for the analysis start, using a precision that matches the original time series. This controls the sample size. Analyze the full period. AnalysisEnd The date/time for the analysis start, using a precision that matches the original time series. This controls the sample size. Analyze the full period. OutputStart The date/time for the output start, using a precision that matches the original time series. The repeating statistic will fill this period. Output the full period. OutputEnd The date/time for the analysis start, using a precision that matches the original time series. The repeating statistic will fill this period. Output the full period. 256 TSTool Documentation NewStatisticTimeSeries() Command Command Reference – NewStatisticTimeSeries() -3 Available Statistics Statistic Description Limitations GeometricMean Geometric mean of all values in the sample. All values must be >= 0. Max Maximum of all values in the sample. None. Mean Arithmetic mean of all values in the sample. None. Median Median of all values in the sample. None. Min Minimum of all values in the sample. None. Examples The following example command file illustrates how to generate test data and a corresponding statistics time series: # Test of computing a statistic time series for monthly data, # Assign 2 months of data so that the mean is different from any month NewPatternTimeSeries(Alias=”ts1”,NewTSID="ts1..Streamflow. Month", Description="Test data",SetStart="1950-01",SetEnd="1951-12",Units="CFS", PatternValues=".5,1.5,,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,1.5,2.5,3.5, 4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5") # Double the above NewPatternTimeSeries(Alias=”ts2”,NewTSID="ts2..Streamflow.Month", Description="Test data",SetStart="1951-01",SetEnd="1952-12",Units="CFS", PatternValues="1.5,3.5,,7.5,9.5,11.5,13.5 ,15.5,17.5,19.5, 21.5,23.5,2.5,4.5,6.5,8.5,10.5,12.5,14.5,16.5,18.5,20.5,22.5,24.5") NewStatisticTimeSeries(TSID="ts1",Alias=”ts1_mean”, NewTSID="ts1..Streamflow.Month.Mean",Statistic=Mean) NewStatisticTimeSeries(TSID="ts2",Alias=”ts2_mean”, NewTSID="ts2..Streamflow.Month.Mean",Statistic=Mean) WriteDateValue(OutputFile="Results\Test_NewStatisticTimeSeries_Month_Mean_out.dv") 257 NewStatisticTimeSeries() Command TSTool Documentation Command Reference – NewStatisticTimeSeries() -4 The following figure illustrates the results. Note that by default the statistic is computed even if missing values exist in the sample. This can be controlled by the AllowMissingCount and MinimumSampleSize parameters. 258 Command Reference – NewStatisticTimeSeriesFromEnsemble() -1 Command Reference: NewStatisticTimeSeriesFromEnsemble() Create a time series containing a statistic determined from a time series ensemble Version 10.18.00, 2013-02-21 The NewStatisticTimeSeriesFromEnsemble() command uses data from time series in an ensemble to calculate a statistic for each interval in the ensemble, and assigns the statistic value to the corresponding interval in the result. For example, for a statistic of Mean applied to a daily time series, all January 1, 1970 values will be used for the sample and the mean value will be assigned to January 1, 1970 in the output time series. Leap year values will be included if they are included in the period of the ensemble. The following dialog is used to edit the command and illustrates the syntax for the command. NewStatisticTimeSeriesFromEnsemble NewStatisticTimeSeriesFromEnsemble() Command Editor The command syntax is as follows: NewStatisticTimeSeriesFromEnsemble(Parameter=Value,…) The following older command syntax is updated to the above syntax when a command file is read: TS Alias = NewStatisticTimeSeriesFromEnsemble(Parameter=Value,…) 259 NewStatisticTimeSeriesFromEnsemble() Command TSTool Documentation Command Reference – NewStatisticTimeSeriesFromEnsemble() -2 Command Parameters Parameter Description Default EnsembleID The identifier for the ensemble to analyze. None – must be specified. NewTSID The time series identifier to be assigned to the new time series, which is useful to avoid confusion with the original time series. This parameter may be required in the future. None – use the same identifier as the original time series. Alias The alias to assign to the time series, as a literal string or using the special formatting characters listed by the command editor. The alias is a short identifier used by other commands to locate time series for processing, as an alternative to the time series identifier (TSID). None – must be specified. Statistic The statistic to compute. See the Available Statistics table below. None – must be specified. Allow Missing Count The number of missing values allowed in the sample of values in order to produce a result. This capability should be used with care because it may result in data that are not representative of actual conditions. Missing values are ignored in the sample used to compute the statistic. MinimumSample Size The minimum number of values in the sample that are required to compute the statistic. Use the sample with no restrictions, although some statistics may have requirements. AnalysisStart The date/time for the analysis start, using a precision that matches the original time series. Analyze the full period. AnalysisEnd The date/time for the analysis start, using a precision that matches the original time series. Analyze the full period. OutputStart The date/time for the output start, using a precision that matches the original time series. An output period longer than the analysis period will result in missing values in output. Output the full period. OutputEnd The date/time for the output start, using a precision that matches the original time series. An output period longer than the analysis period will result in missing values in output. Output the full period. Available Statistics Statistic Description Limitations Exceedance Probability10 The data value corresponding to a 10% chance of value being exceeded. Small sample size will skew – see statistic details. Exceedance Probability30 The data value corresponding to a 30% chance of value being exceeded. Small sample size will skew – see statistic details. Exceedance Probability50 The data value corresponding to a 50% chance of value being exceeded. Small sample size will skew – see statistic details. Exceedance Probability70 The data value corresponding to a 70% chance of value being exceeded. Small sample size will skew – see statistic details. Exceedance The data value corresponding to a 90% Small sample size will skew – 260 TSTool Documentation NewStatisticTimeSeriesFromEnsemble() Command Command Reference – NewStatisticTimeSeriesFromEnsemble() -3 Statistic Description Limitations Probability90 chance of value being exceeded. see statistic details. GeometricMean Geometric mean of all values in the sample. All values must be >= 0. Max Maximum of all values in the sample. None. Mean Arithmetic mean of all values in the sample. None. Median Median of all values in the sample. None. Min Minimum of all values in the sample. None. Missing Count The count of values that are missing. This statistic will be computed regardless of AllowMissingCount and MinimumSampleSize. Missing Percent The percent of values that are missing. See above. Nonmissing Count The count of values that are not missing. See above. Nonmissing Percent The percent of values that are not missing. See above. Total Total of values in the sample. None. Statistic Details Statistic Description Exceedance Probability* The statistic for each time step in the analysis period is computed as follows: 1. The data values are extracted for each trace with missing values being ignored. The sample size is n. 2. The data values are sorted into ascending order. 3. Exceedance probabilities are computed for the number of sample values according to Weibull plotting positions as follows (for i=1,…,n): a. If n = 1, the exceedance probability Pi=1.0. This is an extreme case due to small sample size. b. Otherwise, Pi=(n –( i – 1))/(n + 1). Therefore, when i=1, Pi=n/(n+1) and when i=n, Pi=1/(n+1). The probabilities will be listed from high to low value (the opposite order of the sorted data values). 4. The data value corresponding to the requested probability is calculated by iterating over the probabilities until the calculated probability for a value is less than the requested probability: a. If the first probability satisfies the condition, the computed value is set to the minimum value in the sample (no extrapolating past the end). b. Otherwise, the value is interpolated from the previous and current sample values. If no calculated probability is less than the requested probability, the computed value is set to the maximum value in the sample (no extrapolating past the end). To create an exceedance probability plot, use several commands with different exceedance probability levels (listed low to high). Graphing the time series in a bar graph with BarOverlap=True will draw the bars on top of each other to give the desired appearance. The edges of the colors will represent the specific exceedance probabilities and the colored areas will represent ranges of exceedance probabilities. 261 NewStatisticTimeSeriesFromEnsemble() Command TSTool Documentation Command Reference – NewStatisticTimeSeriesFromEnsemble() -4 Examples The following example command file illustrates how to compute the mean statistic for one monthly data: # Test computing a statistic time series for Month data where Statistic=Mean StartLog(LogFile="Results/Test_NewStatisticTimeSeriesFromEnsemble_ Month_Mean.TSTool.log") # Define 2 years of data that when averaged equal even numbers # The 2nd time series is shifted by 1 from the first. # Include missing values in the first time series but not the second. NewPatternTimeSeries(Alias=”ts1”,NewTSID="ts1..Streamflow.Month", Description="test data 1",SetStart="2000-01",SetEnd="2001-12",Units="CFS", PatternValues=".5,1.5,,3.5,4.5, 5.5,6.5,7.5,8.5,9.5,10.5,11.5, 1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5") NewPatternTimeSeries(Alias=”ts2”,NewTSID="ts2..Streamflow.Month", Description="test data 2",SetStart="2000-01",SetE nd="2001-12",Units="CFS", PatternValues="1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5, 2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5") # Create an ensemble to hold the above