First commit with files as used by Laura for the Dakota-Nek5000 study

#!/usr/bin/env python

# Dakota will execute this script as

#   generic_driver.py params.in results.out

# The command line arguments will be extracted by dakota.interfacing automatically.

# necessary python modules

import os

import sys

import json

sys.path.insert(0, "/home/lpswile/Workbench-Linux/wasppy")

sys.path.insert(0, "/projects/dakota/install/cts/intel/dakota-6.11.0.Linux.x86_64/share/dakota/Python/dakota") # dakota_install/share/dakota/Python/dakota

import waspdrive # Workbench Analysis Sequence Processor driver module

from interfacing import interfacing as di # Dakota's interface module

# ----------------------------

# Parse Dakota parameters file

# ----------------------------

params, results = di.read_parameters_file()

#dump params to external params.json file for future use by the template engine

params_for_template_engine_file_path = "params.json"

with open(params_for_template_engine_file_path, 'w') as outfile:

    f=json.dump(params._variables,  outfile, default=lambda o: o.__dict__)

#note that in object params, _variables is an OrderedDict

#params.descriptors will output this ordered _variables

# -------------------------------

# Pre-processing

# Convert and send to application

# Or copy parameters into template to generate input file

# -------------------------------

# Set up the data structures

continuous_vars = [params[k] for k in params.descriptors]

#continuous_vars are defined in dakota .in file as cdv_descriptor

active_set_vector = 0

# Alternatively, the ASV can be accessed by index in

# function, gradient, hessian order

#for i, bit in enumerate(results["obj_fn"].asv):

#    if bit:

#        active_set_vector += 1 << i

# Obtain the drive module's input

# This file contains the application information and extraction logic

import glob

driver_inputs = glob.glob("*.drive")

if len(driver_inputs) == 0:

    raise ValueError("Unable to find drive file in "+os.getcwd()+"; did you forget to copy or link the file?")

driver_document = waspdrive.process_drive_input(driver_inputs[0])

rtncode=waspdrive.run_external_app(driver_document, params_for_template_engine_file_path)

#follow the logic in ddi file to extract required responses

res_output=waspdrive.extract_results(driver_document)

retval = dict([])

retval['fns']=res_output

# ----------------------------

# Return the results to Dakota

# ----------------------------

# Insert extracted values into results

# Results iterator provides an index, response name, and response

try:

  for i, n, r in results:

    if r.asv.function:

        try:

            r.function = retval['fns'][i]

        except:

            pass

# Catch Dakota 6.9 exception where results interface has changed

# ValueError: too many values to unpack            

except ValueError: 

  i = 0

  for n, r in results.items():

      r.function = retval['fns'][i]

      i+=1

results.write()

#dump to external results.json file

with open('results.json', 'w') as outfile:

    rst=json.dump(results,  outfile, default=lambda o: o.__dict__)

42

0.001906324	0.0	3.0

0.017365762	0.0	3.0

0.032831077	0.0	3.0

0.048290515	0.0	3.0

0.07148555	0.0	3.0

0.094680584	0.0	3.0

0.125605338	0.0	3.0

0.156530091	0.0	3.0

0.187454844	0.0	3.0

0.218379598	0.0	3.0

0.249304351	0.0	3.0

0.280229105	0.0	3.0

0.311153858	0.0	3.0

0.337664148	0.0	3.0

0.360430081	0.0	3.0

0.379980685	0.0	3.0

0.396780333	0.0	3.0

0.411199344	0.0	3.0

0.423584529	0.0	3.0

0.434223914	0.0	3.0

0.443352623	0.0	3.0

0.451194026	0.0	3.0

0.457935046	0.0	3.0

0.463717346	0.0	3.0

0.468683176	0.0	3.0

0.472957153	0.0	3.0

0.476618629	0.0	3.0

0.479761656	0.0	3.0

0.482464413	0.0	3.0

0.484789794	0.0	3.0

0.486785414	0.0	3.0

0.488482425	0.0	3.0

0.489943724	0.0	3.0

0.491201052	0.0	3.0

0.492269693	0.0	3.0

0.493196672	0.0	3.0

0.493998446	0.0	3.0

0.4946739	0.0	3.0

0.495255246	0.0	3.0

0.495742365	0.0	3.0

0.496166706	0.0	3.0

0.496512339	0.0	3.0

non dimensional		non-dimensional

test velocity		wall distance

0.368060173		0.003487661

0.388975193		0.003833294

0.420099889		0.004257635

0.451364665		0.004744754

0.477144771		0.0053261

0.510769356		0.006001554

0.544695652		0.006803328

0.577021804		0.007730307

0.613711986		0.008798948

0.642266753		0.010056276

0.664679551		0.011517575

0.689678441		0.013214586

0.714731209		0.015210206

0.736012592		0.017535587

0.762735544		0.020238344

0.783801419		0.023381371

0.800233879		0.027042847

0.81962957		0.031316824

0.831590246		0.036282654

0.851955722		0.042064954

0.875122797		0.048805974

0.891878519		0.056647377

0.905293872		0.065776086

0.927922178		0.076415471

0.960194452		0.088800656

0.976519159		0.103219667

1.001464172		0.120019315

1.01811214		0.139569919

1.051839092		0.162335852

1.074467398		0.188846142

1.100974842		0.219770895

1.127428409		0.250695649

1.149679577		0.281620402

1.170853206		0.312545156

1.191218681		0.343469909

1.208459296		0.374394662

1.221874648		0.405319416

1.230387202		0.42851445

1.237552832		0.451709485

1.241324216		0.467168923

1.240623816		0.482634238

1.241324216		0.498093676

 No newline at end of file

# DAKOTA INPUT FILE: multidimensional parameter study

environment

	tabular_graphics_data

	  tabular_graphics_file = 'multidim_param_study_tabular_output'

method

        multidim_parameter_study

	partition 2 5 2 

variables

	active all

        discrete_uncertain_set

	  integer 2

	    num_set_values 3 6 

	    set_values 5 10 15 4 6 8 10 12 14

	  descriptor 'filterWeight' 'lx1'

          real 1

            num_set_values 3 

            set_values   0.1 0.5 0.9

	  descriptor       'filterCutoffRatio'

interface

   analysis_drivers = 'python dakota_driver.py'

   fork

   work_directory

     named 'pipe_workdir'

   directory_tag

   directory_save

   copy_files = "pipe_3840.re2"

                "pipe_3840.tmpl"

                "pipe_3840_31000.fld"

                "dakota_driver.py"

                "pipe_3840.drive"

                "pipe_3840.txt"

                "test.py"

                "expPoints.txt"

                "longPipeRe31k.txt"

                "script-postprocess.py"

   replace

   parameters_file = 'params.in'

   results_file = 'results.out'

   file_save file_tag

   asynchronous evaluation_concurrency = 4 

responses

	response_functions = 2 

        descriptors = 'L2_error_norm' 'Friction_velocity'

	no_gradients

	no_hessians

%eval_id	interface	filterWeight	lx1	filterCutoffRatio	L2_error_norm	Friction_velocity

1	NO_ID	5	4	0.1	7.69141E-01	5.30724E-02

2	NO_ID	10	4	0.1	7.93254E-01	5.43443E-02

3	NO_ID	15	4	0.1	8.03741E-01	5.48011E-02

4	NO_ID	5	6	0.1	8.96994E-01	3.70148E-02

5	NO_ID	10	6	0.1	1.21345E+00	3.21973E-02

6	NO_ID	15	6	0.1	1.37012E+00	2.96943E-02

7	NO_ID	5	8	0.1	6.68936E-01	4.13587E-02

8	NO_ID	10	8	0.1	7.64389E-01	4.02307E-02

9	NO_ID	15	8	0.1	7.37930E-01	4.00737E-02

10	NO_ID	5	10	0.1	6.58417E-01	4.57216E-02

11	NO_ID	10	10	0.1	9.07838E-01	4.23209E-02

12	NO_ID	15	10	0.1	1.32545E+00	3.96272E-02

13	NO_ID	5	12	0.1	3.54392E-01	5.00135E-02

14	NO_ID	10	12	0.1	5.06743E-01	4.80353E-02

15	NO_ID	15	12	0.1	4.80349E-01	4.66883E-02

16	NO_ID	5	14	0.1	2.31134E-01	5.46385E-02

17	NO_ID	10	14	0.1	2.11848E-01	5.42959E-02

18	NO_ID	15	14	0.1	2.34419E-01	5.43871E-02

19	NO_ID	5	4	0.5	4.77275E-01	4.79850E-02

20	NO_ID	10	4	0.5	6.34830E-01	4.69166E-02

21	NO_ID	15	4	0.5	7.59374E-01	4.66617E-02

22	NO_ID	5	6	0.5	1.71939E-01	5.35463E-02

23	NO_ID	10	6	0.5	1.90348E-01	5.35463E-02

24	NO_ID	15	6	0.5	2.79961E-01	5.10808E-02

25	NO_ID	5	8	0.5	7.37100E-02	5.71283E-02

26	NO_ID	10	8	0.5	1.05316E-01	5.72736E-02

27	NO_ID	15	8	0.5	1.51721E-01	5.77325E-02

28	NO_ID	5	10	0.5	4.24788E-01	4.86497E-02

29	NO_ID	10	10	0.5	6.04053E-01	4.75098E-02

30	NO_ID	15	10	0.5	7.04380E-01	4.77568E-02

31	NO_ID	5	12	0.5	1.81086E-01	5.32386E-02

32	NO_ID	10	12	0.5	1.87085E-01	5.21777E-02

33	NO_ID	15	12	0.5	2.12426E-01	5.17184E-02

34	NO_ID	5	14	0.5	1.34090E-01	5.53040E-02

35	NO_ID	10	14	0.5	8.16510E-02	5.51992E-02

36	NO_ID	15	14	0.5	1.41064E-01	5.54050E-02

37	NO_ID	5	4	0.9	3.62988E-01	4.95526E-02

38	NO_ID	10	4	0.9	4.92948E-01	4.77818E-02

39	NO_ID	15	4	0.9	5.88040E-01	4.75678E-02

40	NO_ID	5	6	0.9	1.75682E-01	5.35020E-02

41	NO_ID	10	6	0.9	1.96688E-01	5.28538E-02

42	NO_ID	15	6	0.9	2.04472E-01	5.24993E-02

43	NO_ID	5	8	0.9	1.82511E-01	5.46056E-02

44	NO_ID	10	8	0.9	1.16198E-01	5.44259E-02

45	NO_ID	15	8	0.9	2.14883E-01	5.44587E-02

46	NO_ID	5	10	0.9	2.99194E-01	5.03820E-02

47	NO_ID	10	10	0.9	4.90367E-01	4.83177E-02

48	NO_ID	15	10	0.9	5.05702E-01	4.76392E-02

49	NO_ID	5	12	0.9	1.72010E-01	5.41159E-02

50	NO_ID	10	12	0.9	1.75616E-01	5.37207E-02

51	NO_ID	15	12	0.9	1.55308E-01	5.33594E-02

52	NO_ID	5	14	0.9	1.65381E-01	5.50859E-02

53	NO_ID	10	14	0.9	1.92710E-01	5.47444E-02

54	NO_ID	15	14	0.9	1.50911E-01	5.47608E-02