13.8.13. Macro “relauncherCodeFlowrateSequential_TemporaryVar.py”

13.8.13.1. Objective

The goal of this macro is to show how to hide one of the evaluator’s attribute and not to store it in the final dataserver. This is considered when a composition is done for instance, in which many variables might be intermediate needed ones, resulting from an assessor and used as input to one of the following, but of no interest to the user at the end. The flowrate code is provided with Uranie and has been also used and discussed throughout these macros.

13.8.13.2. Macro

"""
Example of code launching in sequential mode with temporary variable
"""
from URANIE import DataServer, Relauncher, Sampler

def increase_d(local_x):
    """Dummy function that increase by one the input."""
    local_y = local_x + 1
    return [local_y, ]


# Create the TDataServer
tds = DataServer.TDataServer("foo", "test")

# Define the attribute that should be considered as constant
r = DataServer.TAttribute("r")

# Add the study attributes (min, max and nominal values)
tds.addAttribute(DataServer.TUniformDistribution("rw", 0.05, 0.15))
tds.addAttribute(DataServer.TUniformDistribution("tu", 63070.0, 115600.0))
tds.addAttribute(DataServer.TUniformDistribution("tl", 63.1, 116.0))
tds.addAttribute(DataServer.TUniformDistribution("hu", 990.0, 1110.0))
tds.addAttribute(DataServer.TUniformDistribution("hl", 700.0, 820.0))
tds.addAttribute(DataServer.TUniformDistribution("l", 1120.0, 1680.0))
tds.addAttribute(DataServer.TUniformDistribution("kw", 9855.0, 12045.0))

# The reference input file
sIn = "flowrate_input_with_keys.in"

nS = 15
# Generate the Design of Experiments
sampling = Sampler.TSampling(tds, "lhs", nS)
sampling.generateSample()

# Create the input files
inputFile = Relauncher.TKeyScript(sIn)
inputFile.addInput(tds.getAttribute("rw"), "Rw")
inputFile.addInput(r, "R")
inputFile.addInput(tds.getAttribute("tu"), "Tu")
inputFile.addInput(tds.getAttribute("tl"), "Tl")
inputFile.addInput(tds.getAttribute("hu"), "Hu")
inputFile.addInput(tds.getAttribute("hl"), "Hl")
inputFile.addInput(tds.getAttribute("l"), "L")
inputFile.addInput(tds.getAttribute("kw"), "Kw")

# Create the output attributes
yhat = DataServer.TAttribute("yhat")
d = DataServer.TAttribute("d")

# Create the output files
outputFile = Relauncher.TKeyResult("_output_flowrate_withKey_.dat")
outputFile.addOutput(yhat, "yhat")
outputFile.addOutput(d, "d")

# Create the user's evaluation function
eval1 = Relauncher.TCodeEval("flowrate -s -k")
eval1.addInputFile(inputFile)
eval1.addOutputFile(outputFile)

# Create a second evaluation function that uses d to change it slightly
incd = DataServer.TAttribute("incd")
eval2 = Relauncher.TPythonEval(increase_d)
eval2.addInput(d)
eval2.addOutput(incd)

# Create the composition
evalC = Relauncher.TComposeEval()
# Add the code one-by-one, in the right order
evalC.addEval(eval1)
evalC.addEval(eval2)

# Create the sequential runner
run = Relauncher.TSequentialRun(evalC)
run.startSlave()  # Start the master (necessary even for a sequential)
if run.onMaster():

    lanceur = Relauncher.TLauncher2(tds, run)
    # State to the master: d is an output attribute and I'm interested in its value
    # but I don't want to keep it in the end. It might be useful for another evaluator
    lanceur.addTemporary(d)
    lanceur.addConstantValue(r, 108)

    # resolution
    lanceur.solverLoop()
    run.stopSlave()  # Stop the slaves (necessary even for a sequential)

tds.scan("*", "", "colsize=6")

Here again, a comparison is drawn with the macro in which we set an attribute to a constant value (see Macro “relauncherCodeFlowrateSequential_ConstantVar.py”), so only the differences are pointed out. The very first one is contained in the beginning lines: a new dummy function, so that we can have a composition of two assessors, this function only adding one to the provided parameter.

def increase_d(local_x):
    """Dummy function that increase by one the input."""
    local_y = local_x + 1
    return [local_y, ]

The rest is exactly as for Macro “relauncherCodeFlowrateSequential_ConstantVar.py”, up to the interface with the newy create function:

# Create a second evaluation function that uses d to change it slightly
incd = DataServer.TAttribute("incd")
eval2 = Relauncher.TPythonEval(increase_d)
eval2.addInput(d)
eval2.addOutput(incd)

# Create the composition
evalC = Relauncher.TComposeEval()
# Add the code one-by-one, in the right order
evalC.addEval(eval1)
evalC.addEval(eval2)

A new output attribute is created, called incd for increased d, and the dummy function is defined as taking d as input and incd as output. Then the composition is done by chaining flowrate with the new dummy function. The rest is fairly common, up to the TMaster-inheriting object specification: the addTemporary method is called to specify that d is read from the output of flowrate and can be pass to the rest of the chain, but it will not be kept in the final dataserver. The addConstantValue is also used just changing the final parameters to show that if nothing is specified, then the value of r is not stored and this might be tricky for bookkeeping. The results is shown in the next section (from the scan method) and can be compared to Console for consistency check.

lanceur = Relauncher.TLauncher2(tds, run)
# State to the master: d is an output attribute and I'm interested in its value
# but I don't want to keep it in the end. It might be useful for another evaluator
lanceur.addTemporary(d)
lanceur.addConstantValue(r, 108)

13.8.13.3. Console

******************************************************************************************************
*    Row   * foo__n *  rw.rw *  tu.tu *  tl.tl *  hu.hu *  hl.hl *    l.l *  kw.kw * yhat.y * incd.i *
******************************************************************************************************
*        0 *      0 * 0.1495 * 111790 * 73.820 * 990.90 * 779.83 * 1474.3 * 11220. * 112.01 * 3589.9 *
*        1 *      1 * 0.1394 * 104140 * 95.150 * 1101.5 * 707.21 * 1422.7 * 11493. * 193.62 * 6598.5 *
*        2 *      2 * 0.0557 * 95387. * 84.809 * 1056.2 * 752.94 * 1184.6 * 11967. * 29.880 * 331.58 *
*        3 *      3 * 0.0836 * 74144. * 103.17 * 1051.6 * 819.27 * 1587.4 * 11031. * 35.400 * 2432.1 *
*        4 *      4 * 0.0586 * 65396. * 72.161 * 1003.1 * 710.34 * 1327.5 * 10484. * 24.990 *   5758 *
*        5 *      5 * 0.1203 * 92149. * 65.263 * 1031.6 * 797.34 * 1265.5 * 11638. * 97.386 * 1085.8 *
*        6 *      6 * 0.1319 * 67464. * 93.378 * 1039.4 * 722.54 * 1514.3 * 10996. * 125.33 * 2363.4 *
*        7 *      7 * 0.1059 * 80448. * 112.87 * 1027.1 * 794.32 * 1555.4 *  11846 * 62.403 * 1117.3 *
*        8 *      8 * 0.0784 * 100260 * 105.79 * 1072.7 * 767.70 * 1304.1 * 10152. * 45.867 * 522.41 *
*        9 *      9 * 0.0697 * 105158 * 82.544 * 1020.4 * 726.05 * 1640.3 * 10380. * 28.412 * 2803.5 *
*       10 *     10 * 0.1252 * 89522. * 100.65 * 1006.2 * 742.35 * 1123.9 * 10743. * 123.70 * 2677.1 *
*       11 *     11 * 0.1165 * 73139. * 69.083 * 1108.5 * 809.59 * 1199.0 * 10620. * 112.27 * 4992.3 *
*       12 *     12 * 0.0992 * 86004. * 112.12 * 1069.4 * 763.84 * 1345.2 * 9951.0 * 69.808 * 415.71 *
*       13 *     13 * 0.0718 * 113775 * 90.580 * 1079.1 * 782.95 * 1416.6 * 10076. * 34.135 * 1017.8 *
*       14 *     14 * 0.0902 * 83779. * 80.244 * 1090.6 * 734.33 * 1644.2 *  11443 * 63.236 * 2923.3 *
******************************************************************************************************