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trick/test/SIM_mc_generation/Modified_data/monte_variables.py

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Integrate MonteCarloGenerate capability from EG CML and associated TrickOps enhancements (#1415) * Provide MonteCarloGenerate capability Intermediate commit, this squash represents all of Isaac Reaves' work during his Fall 2022 Pathways internship tour [skip ci] * TrickOps: Add phase, [min-max] range, and overhaul YAML verification * Add new "phase:" mechanism to TrickOps Runs and Builds to support project-specific constraints on build and run ordering - phase defaults to zero if not specified and must be between -1000 and 1000 if given. - jobs can now optionally be requested by their phase or phase range - See trickops/README.md for details * Add [min-max] notation capability to run: entries and compare: entries - [min-max] ranges provide definition of a set of runs using a common numbering scheme in the YAML file, greatly reducing YAML file size for monte-carlo and other zero-padded run numbering use cases - See trickops/README.md for details * YAML parsing changes - Overhaul the logic which verifies YAML files for the expected TrickOps format. This is now done in TrickWorkflowYamlVerifier and provides much more robust error checking than previous approach - .yaml_requirements.yml now provides the required types, ranges, and default values as applicable to expected entries in YAML files - valgrind: is now an sub-option to run: entries, not its own section Users should now list their runs normallly and define their flags in in that run's valgrind: subsection - parallel_safety is now a per-sim parameter and not global. Users should move their global config to the sim layer - self.config_errors is now a list of errors. Users should now check for empty list when using instead of True/False * Robustify the get_koviz_report_jobs unit test to work whether koviz exists on PATH or not * Adjust trickops.py to use the new phase and range features - Make it more configurable on the command-line via argparse - Move SIM_mc_generation tests into test_sims.yml [skip ci] * Code review and cleanup from PR #1389 Documentation: * Adjust documentation to fit suggested symlinked approach. Also cleaned up duplicate images and old documentation. * Moved the verification section out of markdown and into a PDF since it heavily leverages formatting not available in markdown. * Clarify a couple points on the Darwin Trick install guide * Update wiki to clarify that data recording strings is not supported MCG Code: * Replace MonteCarloVariableRandomNormal::is_near_equal with new Trick::dbl_is_near from trick team MCG Testing: * Reduce the set of SIM_mc_generation comparisons. After discussion the trick team, we are choosing to remove all comparisons to verif_data/ which contain random-generated numbers since these tests cannot pass across all supported trick platforms. * Fix the wrong rule on exlcuding -Werror for Darwin builds of SIM_mc_generation * Remove data recording of strings in SIM_mc_generation Trickops: * Replace build_command with build_args per discussion w/ Trick team Since we only support arguments to trick-CP, replace the build_command yaml entry with build_args * Disable var server connection by default in SingleRun if TrickWorkflow.quiet is True * Guard against multiple Job starts * Remove SimulationJob inheritance layer since old monte-carlo wasn't and never will be supported by TrickOps * Ignore IOError raise from variable_server that looks like "The remote endpoint has closed the connection". This appears to occur when SingleRun jobs attempt to connect to the var server for a sim that terminates very early [skip ci] * Adjust phasing of old/new MCG initialize functions * Clarify failure message in generate_dispersions if new/old MC are both used. * Adjust the phasing order of MCG intialize method to be before legacy MC initialized. Without this, monte-carlo dry run completes with success before the check in generate_dispersions() can run * Add -Wno-stringop-truncation to S_override.mk for SIM_mc_generation since gcc 8+ warns about SWIG generated content in top.cpp * Introduce MonteCarloGenerationHelper python class This new class provides an easy-to-use interface for MCG sim-module users: 1. Run generation 2. Getting an sbatch array job suitable for SLURM 3. Getting a list of SingleRun() instances for generated runs, to be executed locally if desired --------- Co-authored-by: Dan Jordan <daniel.d.jordan@nasa.gov>
2023-03-06 15:25:50 +00:00
mc_var = trick.MonteCarloVariableRandomUniform( "test.x_uniform", 0, 10, 20)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal", 2, 10, 2)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_trunc[0]", 2, 10, 2)
mc_var.truncate(0.5, trick.MonteCarloVariableRandomNormal.StandardDeviation)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_trunc[1]", 2, 10, 2)
mc_var.truncate(-0.5, 0.7, trick.MonteCarloVariableRandomNormal.Relative)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_trunc[2]", 2, 10, 2)
mc_var.truncate(9.9,11, trick.MonteCarloVariableRandomNormal.Absolute)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_trunc[3]", 2, 10, 2)
mc_var.truncate_low(9.9, trick.MonteCarloVariableRandomNormal.Absolute)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_trunc[4]", 2, 10, 2)
mc_var.truncate_high(4, trick.MonteCarloVariableRandomNormal.Absolute)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomNormal( "test.x_normal_length", 2, 10, 2)
mc_var.units = "ft"
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomUniformInt( "test.x_integer", 1, 0, 2)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomStringSet( "test.x_string", 3)
mc_var.add_string("\"ABC\"")
mc_var.add_string("\"DEF\"")
mc_var.add_string("'GHIJKL'")
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloPythonLineExec( "test.x_line_command",
"test.x_integer * test.x_uniform")
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloPythonLineExec(
"test.standalone_function( test.x_normal)")
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloPythonFileExec( "Modified_data/sample.py")
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableRandomBool( "test.x_boolean", 4)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
# call this one mc_var1 because I'm going to use it as the seed for the
# MonteCarloVariableSemiFixed later.
mc_var1 = trick.MonteCarloVariableFile( "test.x_file_lookup[0]",
"Modified_data/datafile.txt",
3)
mc_var1.thisown = False
monte_carlo.mc_master.add_variable(mc_var1)
mc_var = trick.MonteCarloVariableFile( "test.x_file_lookup[1]",
"Modified_data/datafile.txt",
2)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableFile( "test.x_file_lookup[2]",
"Modified_data/datafile.txt",
1)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableFixed( "test.x_fixed_value_int", 7)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableFixed( "test.x_fixed_value_double", 7.0)
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableFixed( "test.x_fixed_value_string", "\"7\"")
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
mc_var = trick.MonteCarloVariableSemiFixed( "test.x_semi_fixed_value", mc_var1 )
mc_var.thisown = False
monte_carlo.mc_master.add_variable(mc_var)
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