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9099792947
* 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>
84 lines
2.8 KiB
C++
84 lines
2.8 KiB
C++
/*******************************TRICK HEADER******************************
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PURPOSE: ( Base class for the MonteCarloVariable type)
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LIBRARY DEPENDENCY:
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((../src/mc_variable.cc))
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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_VARIABLE_HH
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#define CML_MONTE_CARLO_VARIABLE_HH
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#include <string>
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class MonteCarloVariable
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{
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public:
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enum MonteCarloVariableType
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{
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Undefined = 0,
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Calculated,
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Constant,
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Execute,
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Prescribed,
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Random
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};
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std::string units; /* (--)
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optional setting in the case where the specified values are in units
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different from the native units of the variable.
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These are the units associated with the specified-value.*/
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bool include_in_summary; /* (--)
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Flag telling MonteCarloMaster whether to include this variable in the
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dispersion summary file. The default depends on the type of variable but
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is generally true. */
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protected:
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std::string variable_name; /* (--)
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The name of the sim-variable being assigned by this instance. */
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std::string assignment; /* (--)
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The value assigned to the variable. Used in MonteCarloMaster to generate
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the dispersion summary file. */
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std::string command; /* (--)
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the command that gets pushed to the monte_input input file.*/
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MonteCarloVariableType type; /* (--)
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Broad categorization of types of MonteCarloVariable. This is set in the
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constructor of the specific classes derived from MonteCarloVariable and
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provides information to the MonteCarloMaster about what general type of
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variable it is dealing with.*/
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public:
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MonteCarloVariable( const std::string & var_name);
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virtual ~MonteCarloVariable() {};
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virtual void generate_assignment() = 0;
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virtual void shutdown() {}; // deliberately empty
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// These getters are intended to be used by the MonteCarloMaster class in
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// preparing the input files and summary data files. They may also be used
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// in the user interface, but -- especially get_assignment and get_command --
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// have limited use there.
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const std::string & get_command() const {return command;}
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const std::string & get_variable_name() const {return variable_name;}
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const std::string & get_assignment() const {return assignment;}
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MonteCarloVariableType get_type() const {return type;}
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virtual unsigned int get_seed() const {return 0;}
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protected:
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void insert_units();
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void trick_units(size_t);
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void assign_double(double value);
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void assign_int(int value);
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void generate_command();
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private: // and undefined:
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MonteCarloVariable( const MonteCarloVariable &);
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MonteCarloVariable& operator = (const MonteCarloVariable&);
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};
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#endif
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