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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>
82 lines
3.3 KiB
C++
82 lines
3.3 KiB
C++
/*******************************TRICK HEADER******************************
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PURPOSE: (
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Implementation of a semi-fixed monte-carlo variable.
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The value of a MonteCarloVariableFixed instance is assigned at
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construction time and held at that value for all runs.
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The value of a MonteCarloVariableSemiFixed instance is assigned
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from another MonteCarloVariable generated value for the first
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input file generated, and held at that value for all runs. So the
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assignment to a Semi-fixed can change each time the input files are
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generated, but it is the same for all input files in any given
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generation.)
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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_SEMI_FIXED_HH
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#define CML_MONTE_CARLO_VARIABLE_SEMI_FIXED_HH
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#include "mc_variable_random.hh"
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#include "trick/message_proto.h"
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#include "trick/message_proto.h"
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// TODO Turner 2019/11
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// The reference to a MonteCarloVariable might be difficult to
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// obtain because these are typically created on-the-fly and
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// the handle to the created instance is lost. Might be
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// nice to provide the seed-variable-name instead of the reference
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// to the seed-variable itself, and have the MonteCarloMaster find
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// the MonteCarloVariable associated with that name.
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// But that's non-trivial and not necessarily desirable, so it is
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// left unimplemented.
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class MonteCarloVariableSemiFixed : public MonteCarloVariable
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{
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protected:
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const MonteCarloVariable & seed_variable; /* (--)
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A reference to another MonteCarloVariable; the value of *this*
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variable is taken from the value of this seed-variable during
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preparation of the first monte-input file. */
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bool command_generated; /* (--)
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flag indicating the fixed command has been generated.*/
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public:
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MonteCarloVariableSemiFixed(const std::string & var_name,
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const MonteCarloVariable & seed_)
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:
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MonteCarloVariable( var_name),
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seed_variable(seed_),
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command_generated(false)
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{
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type = MonteCarloVariable::Constant;
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}
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virtual ~MonteCarloVariableSemiFixed(){};
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void generate_assignment() {
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if (!command_generated) {
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// parse the command from seed_variable to get the assignment.
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std::string seed_command = seed_variable.get_command();
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size_t pos_equ = seed_command.find("=");
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if (pos_equ == std::string::npos) {
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std::string message =
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std::string("File: ") + __FILE__ +
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", Line: " + std::to_string(__LINE__) + std::string(", Invalid "
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"sequencing\nFor variable ") + variable_name.c_str() +
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std::string(" the necessary pre-dispersion to obtain the\n "
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"random value for assignment has not been completed.\nCannot "
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"generate the assignment for this variable.\n");
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message_publish(MSG_ERROR, message.c_str());
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return;
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}
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assignment = seed_command.substr(pos_equ+1);
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generate_command();
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insert_units();
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command_generated = true;
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}
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}
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private: // and undefined:
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MonteCarloVariableSemiFixed( const MonteCarloVariableSemiFixed & );
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MonteCarloVariableSemiFixed& operator = (const MonteCarloVariableSemiFixed&);
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};
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#endif
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