discipline module¶
Abstraction of processes¶
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class
gemseo.core.discipline.
MDODiscipline
(name=None, input_grammar_file=None, output_grammar_file=None, auto_detect_grammar_files=False, grammar_type='JSON', cache_type='SimpleCache', cache_file_path=None)[source]¶ Bases:
object
A software integrated in the workflow.
The inputs and outputs are defined in a grammar, which can be either a SimpleGrammar or a JSONGrammar, or your own which derives from the Grammar abstract class
To be used, use a subclass and implement the _run method which defined the execution of the software. Typically, in the _run method, get the inputs from the input grammar, call your software, and write the outputs to the output grammar.
The JSON Grammar are automatically detected when in the same folder as your subclass module and named “CLASSNAME_input.json” use auto_detect_grammar_files=True to activate this option
Constructor.
- Parameters
name – the name of the discipline
input_grammar_file – the file for input grammar description, if None, name + “_input.json” is used
output_grammar_file – the file for output grammar description, if None, name + “_output.json” is used
auto_detect_grammar_files – if no input and output grammar files are provided, auto_detect_grammar_files uses a naming convention to associate a grammar file to a discipline: searches in the “comp_dir” directory containing the discipline source file for files basenames self.name _input.json and self.name _output.json
grammar_type – the type of grammar to use for IO declaration either JSON_GRAMMAR_TYPE or SIMPLE_GRAMMAR_TYPE
cache_type – type of cache policy, SIMPLE_CACHE or HDF5_CACHE
cache_file_path – the file to store the data, mandatory when HDF caching is used
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APPROX_MODES
= ['finite_differences', 'complex_step']¶
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AVAILABLE_MODES
= ('auto', 'direct', 'adjoint', 'reverse', 'finite_differences', 'complex_step')¶
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COMPLEX_STEP
= 'complex_step'¶
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FINITE_DIFFERENCES
= 'finite_differences'¶
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HDF5_CACHE
= 'HDF5Cache'¶
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JSON_GRAMMAR_TYPE
= 'JSON'¶
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MEMORY_FULL_CACHE
= 'MemoryFullCache'¶
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N_CPUS
= 2¶
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RE_EXECUTE_DONE_POLICY
= 'RE_EXEC_DONE'¶
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RE_EXECUTE_NEVER_POLICY
= 'RE_EXEC_NEVER'¶
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SIMPLE_CACHE
= 'SimpleCache'¶
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SIMPLE_GRAMMAR_TYPE
= 'Simple'¶
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STATUS_DONE
= 'DONE'¶
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STATUS_FAILED
= 'FAILED'¶
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STATUS_PENDING
= 'PENDING'¶
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STATUS_RUNNING
= 'RUNNING'¶
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STATUS_VIRTUAL
= 'VIRTUAL'¶
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add_differentiated_inputs
(inputs=None)[source]¶ Add inputs to the differentiation list.
This method updates self._differentiated_inputs with inputs
- Parameters
inputs – list of inputs variables to differentiate if None, all inputs of discipline are used (Default value = None)
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add_differentiated_outputs
(outputs=None)[source]¶ Add outputs to the differentiation list.
Update self._differentiated_inputs with inputs.
- Parameters
outputs – list of output variables to differentiate if None, all outputs of discipline are used
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add_status_observer
(obs)[source]¶ Add an observer for the status
Add an observer for the status to be notified when self changes of status.
- Parameters
obs – the observer to add
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auto_get_grammar_file
(is_input=True, name=None, comp_dir=None)[source]¶ Use a naming convention to associate a grammar file to a discipline.
This method searches in the “comp_dir” directory containing the discipline source file for files basenames self.name _input.json and self.name _output.json
- Parameters
is_input – if True, searches for _input.json, otherwise _output.json (Default value = True)
name – the name of the discipline (Default value = None)
comp_dir – the containing directory if None, use self.comp_dir (Default value = None)
- Returns
path to the grammar file
- Return type
string
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property
cache_tol
¶ Accessor to the cache input tolerance.
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check_input_data
(input_data, raise_exception=True)[source]¶ Check the input data validity.
- Parameters
input_data – the input data dict
raise_exception – Default value = True)
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check_jacobian
(input_data=None, derr_approx='finite_differences', step=1e-07, threshold=1e-08, linearization_mode='auto', inputs=None, outputs=None, parallel=False, n_processes=2, use_threading=False, wait_time_between_fork=0, auto_set_step=False, plot_result=False, file_path='jacobian_errors.pdf', show=False, figsize_x=10, figsize_y=10)[source]¶ Check if the jacobian provided by the linearize() method is correct.
- Parameters
input_data – input data dict (Default value = None)
derr_approx – derivative approximation method: COMPLEX_STEP (Default value = COMPLEX_STEP)
threshold – acceptance threshold for the jacobian error (Default value = 1e-8)
linearization_mode – the mode of linearization: direct, adjoint or automated switch depending on dimensions of inputs and outputs (Default value = ‘auto’)
inputs – list of inputs wrt which to differentiate (Default value = None)
outputs – list of outputs to differentiate (Default value = None)
step – the step for finite differences or complex step
parallel – if True, executes in parallel
n_processes – maximum number of processors on which to run
use_threading – if True, use Threads instead of processes to parallelize the execution multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing
wait_time_between_fork – time waited between two forks of the process /Thread
auto_set_step – Compute optimal step for a forward first order finite differences gradient approximation
plot_result – plot the result of the validation (computed and approximate jacobians)
file_path – path to the output file if plot_result is True
show – if True, open the figure
figsize_x – x size of the figure in inches
figsize_y – y size of the figure in inches
- Returns
True if the check is accepted, False otherwise
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check_output_data
(raise_exception=True)[source]¶ Check the output data validity.
- Parameters
raise_exception – if true, an exception is raised when data is invalid (Default value = True)
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property
default_inputs
¶ Accessor to the default inputs.
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static
deserialize
(in_file)[source]¶ Derialize the discipline from a file.
- Parameters
in_file – input file for serialization
- Returns
a discipline instance
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property
exec_time
¶ Return the cumulated execution time.
Multiprocessing safe.
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execute
(input_data=None)[source]¶ Execute the discipline.
This method executes the discipline:
- Adds default inputs to the input_data if some inputs are not defined
in input_data but exist in self._default_data
- Checks if the last execution of the discipline wan not called with
identical inputs, cached in self.cache, if yes, directly return self.cache.get_output_cache(inputs)
Caches the inputs
Checks the input data against self.input_grammar
if self.data_processor is not None: runs the preprocessor
updates the status to RUNNING
calls the _run() method, that shall be defined
if self.data_processor is not None: runs the postprocessor
checks the output data
Caches the outputs
updates the status to DONE or FAILED
updates summed execution time
- Parameters
input_data (dict) – the input data dict needed to execute the disciplines according to the discipline input grammar (Default value = None)
- Returns
the discipline local data after execution
- Return type
dict
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get_all_inputs
()[source]¶ Accessor for the input data as a list of values.
The order is given by self.get_input_data_names().
- Returns
the data
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get_all_outputs
()[source]¶ Accessor for the output data as a list of values.
The order is given by self.get_output_data_names().
- Returns
the data
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get_attributes_to_serialize
()[source]¶ Define the attributes to be serialized.
Shall be overloaded by disciplines
- Returns
the list of attributes names
- Return type
list
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static
get_data_list_from_dict
(keys, data_dict)[source]¶ Filter the dict from a list of keys or a single key.
If keys is a string, then the method return the value associated to the key. If keys is a list of string, then the method return a generator of value corresponding to the keys which can be iterated.
- Parameters
keys – a sting key or a list of keys
data_dict – the dict to get the data from
- Returns
a data or a generator of data
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get_expected_dataflow
()[source]¶ Return the expected data exchange sequence.
This method is used for the XDSM representation.
Default to empty list See MDOFormulation.get_expected_dataflow
- Returns
a list representing the data exchange arcs
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get_expected_workflow
()[source]¶ Return the expected execution sequence.
This method is used for XDSM representation Default to the execution of the discipline itself See MDOFormulation.get_expected_workflow
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get_input_output_data_names
()[source]¶ Accessor for the input and output names as a list.
- Returns
the data names list
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get_inputs_asarray
()[source]¶ Accessor for the outputs as a large numpy array.
The order is the one of self.get_all_outputs().
- Returns
the outputs array
- Return type
ndarray
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get_inputs_by_name
(data_names)[source]¶ Accessor for the inputs as a list.
- Parameters
data_names – the data names list
- Returns
the data list
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get_local_data_by_name
(data_names)[source]¶ Accessor for the local data of the discipline as a dict of values.
- Parameters
data_names – the names of the data which will be the keys of the dictionary
- Returns
the data list
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get_output_data_names
()[source]¶ Accessor for the output names as a list.
- Returns
the data names list
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get_outputs_asarray
()[source]¶ Accessor for the outputs as a large numpy array.
The order is the one of self.get_all_outputs()
- Returns
the outputs array
- Return type
ndarray
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get_outputs_by_name
(data_names)[source]¶ Accessor for the outputs as a list.
- Parameters
data_names – the data names list
- Returns
the data list
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get_sub_disciplines
()[source]¶ Gets the sub disciplines of self By default, empty
- Returns
the list of disciplines
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is_all_inputs_existing
(data_names)[source]¶ Test if all the names in data_names are inputs of the discipline.
- Parameters
data_names – the names of the inputs
- Returns
True if data_names are all in input grammar
- Return type
logical
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is_all_outputs_existing
(data_names)[source]¶ Test if all the names in data_names are outputs of the discipline.
- Parameters
data_names – the names of the outputs
- Returns
True if data_names are all in output grammar
- Return type
logical
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is_input_existing
(data_name)[source]¶ Test if input named data_name is an input of the discipline.
- Parameters
data_name – the name of the output
- Returns
True if data_name is in input grammar
- Return type
logical
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is_output_existing
(data_name)[source]¶ Test if output named data_name is an output of the discipline.
- Parameters
data_name – the name of the output
- Returns
True if data_name is in output grammar
- Return type
logical
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property
linearization_mode
¶ Accessor to the linearization mode.
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linearize
(input_data=None, force_all=False, force_no_exec=False)[source]¶ Execute the linearized version of the code.
- Parameters
input_data – the input data dict needed to execute the disciplines according to the discipline input grammar
force_all – if False, self._differentiated_inputs and self.differentiated_output are used to filter the differentiated variables otherwise, all outputs are differentiated wrt all inputs (Default value = False)
force_no_exec – if True, the discipline is not re executed, cache is loaded anyway
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property
n_calls
¶ Return the number of calls to execute() which triggered the _run().
Multiprocessing safe.
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property
n_calls_linearize
¶ Return the number of calls to linearize() which triggered the _compute_jacobian() method.
Multiprocessing safe.
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remove_status_observer
(obs)[source]¶ Remove an observer for the status.
- Parameters
obs – the observer to remove
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serialize
(out_file)[source]¶ Serialize the discipline.
- Parameters
out_file – destination file for serialization
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set_cache_policy
(cache_type='SimpleCache', cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_name=None)[source]¶ Set the type of cache to use and the tolerance level.
This method set the cache policy to cache data whose inputs are close to inputs whose outputs are already cached. The cache can be either a simple cache recording the last execution or a full cache storing all executions. Caching data can be either in-memory, e.g.
SimpleCache
andMemoryFullCache
, or on the disk, e.g.HDF5Cache
.CacheFactory.caches
provides the list of available types of caches.- Parameters
cache_type (str) – type of cache to use.
cache_tolerance (float) – tolerance for the approximate cache maximal relative norm difference to consider that two input arrays are equal
cache_hdf_file (str) – the file to store the data, mandatory when HDF caching is used
cache_hdf_node_name (str) – name of the HDF dataset to store the discipline data. If None, self.name is used
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set_disciplines_statuses
(status)[source]¶ Set the sub disciplines statuses.
To be implemented in subclasses. :param status: the status
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set_jacobian_approximation
(jac_approx_type='finite_differences', jax_approx_step=1e-07, jac_approx_n_processes=1, jac_approx_use_threading=False, jac_approx_wait_time=0)[source]¶ Set the jacobian approximation method.
Sets the linearization mode to approx_method, sets the parameters of the approximation for further use when calling self.linearize
- Parameters
jac_approx_type – “complex_step” or “finite_differences”
jax_approx_step – the step for finite differences or complex step
jac_approx_n_processes – maximum number of processors on which to run
jac_approx_use_threading – if True, use Threads instead of processes to parallelize the execution multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing
jac_approx_wait_time – time waited between two forks of the process /Thread
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set_optimal_fd_step
(outputs=None, inputs=None, force_all=False, print_errors=False, numerical_error=2.220446049250313e-16)[source]¶ Compute the optimal finite-difference step.
Compute the optimal step for a forward first order finite differences gradient approximation. Requires a first evaluation of perturbed functions values. The optimal step is reached when the truncation error (cut in the Taylor development), and the numerical cancellation errors (roundoff when doing f(x+step)-f(x)) are approximately equal.
Warning: this calls the discipline execution two times per input variables.
See: https://en.wikipedia.org/wiki/Numerical_differentiation and “Numerical Algorithms and Digital Representation”, Knut Morken , Chapter 11, “Numerical Differenciation”
- Parameters
inputs – inputs wrt the linearization is made. If None, use differentiated inputs
outputs – outputs of the linearization is made. If None, use differentiated outputs
force_all – if True, all inputs and outputs are used
print_errors – if True, displays the estimated errors
numerical_error – numerical error associated to the calculation of f. By default Machine epsilon (appx 1e-16), but can be higher when the calculation of f requires a numerical resolution
- Returns
the estimated errors of truncation and cancelation error.
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property
status
¶ Status accessor.