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* 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>
116 lines
4.1 KiB
C++
116 lines
4.1 KiB
C++
/*******************************TRICK HEADER******************************
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PURPOSE: ( Implementation of a simple python executable code instruction)
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LIMITATION: (This implementation is intended to provide a fixed operation
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that generates one variable as a function of one or more
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variables -- such as random variables -- that were previously
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generated by the MonteCarlo process.
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E.g. y = x + 2
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It does not provide randomization of the operation itself; in
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this example the value y will always be generated as x+2, using
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the value x which may be variable. However, it will never be
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generated as x+3.
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To implement randomization of the operation itself, use the
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MonteCarloVariableRandomStringSet class instead.)
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PROGRAMMERS:
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(((Gary Turner) (OSR) (October 2019) (Antares) (Initial)))
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(((Isaac Reaves) (NASA) (November 2022) (Integration into Trick Core)))
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**********************************************************************/
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#ifndef CML_MONTE_CARLO_PYTHON_CODE_HH
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#define CML_MONTE_CARLO_PYTHON_CODE_HH
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#include "mc_variable.hh"
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class MonteCarloPythonLineExec : public MonteCarloVariable
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{
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public:
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std::string instruction_set; /* (--)
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The right-hand-side of an equation that gets inserted into the
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monte-input file and looks like:
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<variable_name> = <instruction-set>*/
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protected:
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bool instruction_is_command; /* (--)
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Indicates whether to implement a command that looks like:
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- variable=instruction vs
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variable representing the standalone command, in which case
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variable_name and instruction_set are identical. */
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public:
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// 2 constructors:
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MonteCarloPythonLineExec(const std::string & var_name,
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const std::string & instruction)
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:
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MonteCarloVariable( var_name),
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instruction_set(instruction),
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instruction_is_command(false)
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{
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include_in_summary = false;
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type = MonteCarloVariable::Calculated;
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}
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// other constructor
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MonteCarloPythonLineExec( const std::string & instruction)
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:
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MonteCarloVariable( instruction),
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instruction_set(instruction),
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instruction_is_command(true)
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{
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include_in_summary = false;
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type = MonteCarloVariable::Execute;
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}
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virtual ~MonteCarloPythonLineExec(){};
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void generate_assignment()
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{
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if (instruction_is_command) {
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command = "\n" + instruction_set;
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}
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else {
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command = "\n" + variable_name + " = " + instruction_set;
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}
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}
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private: // and undefined:
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MonteCarloPythonLineExec( const MonteCarloPythonLineExec & );
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MonteCarloPythonLineExec& operator = (const MonteCarloPythonLineExec&);
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};
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/*****************************************************************************
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MonteCarloPythonFileExec
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Purpose:(Provides a filename for execution to support more extensive
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calculations than are possible with the simple one-liner commands
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provided by MonteCarloPythonLineExec)
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Assumptions: The file identified by filename is expected to be a Python file
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Limitations: The file is not tested prior to execution
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Other notes:
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This class inherits from MonteCarloVariable to simplify the inclusion of
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its "command" into the monte_input files. However, it does not populate
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a specific variable; its command string is an executive statement, unlike
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other MonteCarloVariable instances, which have a command string that
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looks like "variable = ..."
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This class's "variable_name" is instead re-purposed as a filename
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*****************************************************************************/
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class MonteCarloPythonFileExec : public MonteCarloVariable
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{
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public:
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MonteCarloPythonFileExec(const std::string & filename)
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:
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MonteCarloVariable( filename)
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{
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include_in_summary = false;
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type = MonteCarloVariable::Execute;
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}
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virtual ~MonteCarloPythonFileExec(){};
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void generate_assignment()
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{
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command =
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"\nexec(open('" + variable_name + "').read())";
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}
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private: // and undefined:
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MonteCarloPythonFileExec( const MonteCarloPythonFileExec & );
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MonteCarloPythonFileExec& operator = (const MonteCarloPythonFileExec&);
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};
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#endif
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