Welcome to KADMOS’s documentation!

See sidebar on the right for the table of contents.

Documentation for the Code

Below the simplified class diagram of the KADMOS package is shown (not all methods are included for clarity). The UML can als be downloaded as PDF here.

_images/KADMOS_UML.png

Example scripts for using KADMOS are available in the examples/scripts folder. The following scripts are available there:

  • Sellar Problem: Small two-disciplinary analytical MDO problem based on MDO literature.
  • Super-sonic Business Jet: The jet design is decribed at http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19980234657.pdf and was implemented in Python for OpenMDAO by Sylvain Dubreuil and Remi Lafage of ONERA, the French Aerospace Lab.
  • TU Delft Wing Design: The wing design case described here was developed at TU Delft and takes a variety of different disciplines into account.

Graph

Below the KADMOS KadmosGraph class and its subclasses are listed.

KadmosGraph

class kadmos.graph.graph_kadmos.KadmosGraph(*args, **kwargs)
add_contact(name, email, uid, company=None, department=None, function=None, address=None, telephone=None, country=None, roles=None)

Method to add a contact to the graph organization.

Parameters:
  • name (str) – name of the contact
  • email (str) – email adress of the contact
  • uid (str) – uid of the contact
  • company (str) – company of which the contact is an employee, optional
  • department (str) – department of company that the contact works at, optional
  • function (str) – the function of the contact, optional
  • address (str) – company address, optional
  • telephone (int) – telephone number of the contact, optional
  • country (str) – country of company, optional
  • roles – organizational role(s) of contact within the project, optional
Type:

str, list

Optional organizational contact roles:

  • ‘architect’
  • ‘integrator’
  • ‘collaborativeEngineer’
  • ‘toolSpecialist’
  • ‘customer’

Adding a contact with one organizational role:

>>> add_contact('Maaike de Wit', 'M.D.deWit@student.tudelft.nl', 'mddewit', company='TU Delft',
>>>             roles='integrator')

Adding a contact with two (or more) organizational roles:

>>> roles = ['integrator', 'architect']
>>> add_contact('Maaike de Wit', 'M.D.deWit@student.tudelft.nl', 'mddewit', company='TU Delft', roles=roles)

Note

In case the contact uid already exists the old contact attributes are conserved and only new roles are added to the roles already present for that contact. To change old contact information, this has to be adjusted in the XML-file from which the old contact information is loaded.

Hint

To add (more) roles to the existing contact, it is recommended to use the method: add_contact_roles()

add_contact_roles(uid, roles)

Method to add roles to existing contacts

Parameters:
  • uid (str) – uid of the contact
  • roles (str, list) – organizational role(s) to be added to the existing contact

Optional organizational contact roles:

  • ‘architect’
  • ‘integrator’
  • ‘collaborativeEngineer’
  • ‘toolSpecialist’
  • ‘customer’

Adding an organizational role to an existing contact:

>>> graph.add_contact('mddewit', roles='integrator')

Adding two (or more) organizational roles to an existing contact:

>>> roles = ['integrator', 'architect']
>>> graph.add_contact('mddewit', company='TU Delft', roles=roles)

Hint

This method only works for existing contacts. To add a contact use the method: add_contact()

add_dc_general_info(dc_uid, description, status=None, creation_date=None, owner_uid=None, creator_uid=None, operator_uid=None, model_definition=None)

Method to add general info to a design competence

Parameters:
  • dc_uid (str) – uid of the design competence
  • description (str) – description of the design competence
  • status (str) – status of the design competence, optional
  • creation_date (date) – creation date of the design competence, optional
  • owner_uid (str) – uid of the owner of the design competence, optional
  • creator_uid (str) – uid of the creator of the design competence, optional
  • operator_uid (str) – uid of the operator of the design competence, optional
  • model_definition (str) – model definition of the design competence, optional
add_dc_performance_info(dc_uid, precision=None, fidelity_level=None, run_time=None, verification=None)

Method to add performance information to a design competence

Parameters:
  • dc_uid (str) – uid of the design competence
  • precision (float) – precision of the design competence, optional
  • fidelity_level (int) – the level of fidelity of the design competence, optional
  • run_time (float) – the run time of the design competence, optional
  • verification (dict) – verification method of the design competence, optional

At least one of the optional elements for the performance information has to be defined.

Adding performance info with a verification:

>>> verification = {'method': 'dummy_method', 'verifier': contact_uid, 'result': 'dummy_result',
>>>                 'date': dateTime, 'version': dummy_version}
>>> graph.add_dc_performance_info(dc_uid, verification=verification)
add_dc_remote_component_info(dc_uid, single_or_multi_execution, job_name, remote_engineer, notification_message, data_exchange_dict=None)

Method to add execution information to a design competence

Parameters:
  • dc_uid (str) – uid of the design competence
  • single_or_multi_execution (str) – execution type. Choose from ‘single’ or ‘multiple’.
  • job_name (str) – job name of the design competence
  • remote_engineer (str) – contact uid of remote engineering of the design competence
  • notification_message (str) – message to notify remote_engineer
  • data_exchange_dict (str) – data exchange settings of the design competence

Example use:

>>> single_or_multi = "single"
>>> job_name = 'job_{}'.format(fpg.nodes[node]['label'].replace(' ', ''))
>>> notification_message = 'Hi, could you please run this tool {} for me for my {} AGILE workflow '
>>>                        'execution. Thanks.'.format(fpg.nodes[node]['label'].replace(' ', ''), architecture)
>>> fpg.add_dc_remote_component_info(node, single_or_multi, job_name, 'ivangent', notification_message,
>>>                                  data_exchange_dict={'urlsite': 'some_url',
>>>                                                      'folder': 'some_folder'})
add_default_description()

Method to add a default description attribute to a graph

Returns:graph with default attribute description
Return type:KadmosGraph
add_default_name()

Method to add a default name attribute to a graph

Returns:graph with default attribute name
Return type:KadmosGraph
add_edge(u, v, attr_dict=None, **attr)

Add an edge between u and v.

Parameters:
  • u (can be, for example, strings or numbers. Nodes must be hashable (and not None) Python objects.) – node
  • v (see u) – node
  • attr_dict (str) – keyword arguments
  • attr (str) – edge data (or labels or objects) can be assigned using keyword arguments.

The nodes u and v will be automatically added if they are not already in the graph.

Adding an existing edge results in an update of the edge data.

Edge attributes can be specified with keywords or by directly accessing the edge’s attribute dictionary. See examples below.

The following examples both add the edge e=(1,2) to graph G:

>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> e = (1, 2)
>>> G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes

Many NetworkX algorithms designed for weighted graphs use an edge attribute (by default ‘weight’) to hold a numerical value. Associate date to edge using keywords:

>>> G.add_edge(1, 2, weight=3)
>>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)

For non-string attribute keys, use subscript notation.

>>> G.add_edge(1, 2)
>>> G[1][2}.update({0: 5})
>>> G.edges[1, 2].update({0: 5})
add_instance(node, instance=None)

Method to change the instance of a node

Parameters:
  • node (str) – node to change the instance of
  • instance (int) – new instance, optional (default=None)

In case the default is used the instance added is the highest current instance + 1.

add_node(n, attr_dict=None, **attr)

Add a single node and update node attributes.

Parameters:
  • n (A node can be any hashable Python object except None.) – node
  • attr_dict (dict) – dictionary of attribute keyword arguments.
  • attr (str, dict) – Set or change node attributes using attr_dict.

Examples:

>>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_node(1)
>>> G.add_node('Hello')
>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
>>> G.add_node(K3)
>>> G.number_of_nodes()

Use keywords set/change node attributes:

>>> G.add_node(1, size=10)
>>> G.add_node(3, weight=0.4, UTM=('13S', 382871, 3972649))

Note

A hashable object is one that can be used as a key in a Python dictionary. This includes strings, numbers, tuples of strings and numbers, etc.

Note

On many platforms hashable items also include mutables such as NetworkX Graphs, though one should be careful that the hash doesn’t change on mutables.

add_objective_function_by_nodes(*args, **kwargs)

This function adds objective functions to the graph using lists of variable nodes.

Parameters:
  • args (list) – list of nodes (list elements must be strings)
  • kwargs (list) – list of nodes (list elements must be strings)
Returns:

list of Objective Functions added to Graph
Return type:

list

Each list produces a separate objective function node in the graph. If the list if passed as a keyword argument, the keyword is taken as the name of the objective function node. Otherwise, a standard name will be given to the node. Each objective function node has one output variable, and takes the nodes given in the argument list as input nodes.

If the provided nodes do not exist in the graph, a warning is given to the user on whether to continue the addition of the objective function to the graph using valid nodes.

Example:

>>> unnamed_function = list(>>list of graph nodes<<)
>>> named_obj_fcn = list(>>list of graph nodes<<)
>>> MyGraph.add_objective_function_by_nodes(unnamed_function, My_obj_fcn_name = named_obj_fcn)

The added objective function nodes can be queried by the attribute:

>>> Graph.nodes[node]["name"] == "Objective"
change_graph_class(graph_class)

Method to adjust the class of a graph.

Returns:newly classed graph.
Return type:graph_class
check(raise_error=False)

Method to check the graph for validity and completeness.

Parameters:raise_error (bool) – determines if an error should be raised in case of an invalid graph
Returns:result of the check
Return type:bool

Note

Several checks are performed. However the method does not guarantee the validity of the graph.

The checks are split into several categories and the methods _check_category_a, _check_category_b and _check_category_c are used to determine the overall validity and completeness. These sub methods are generally defined below and are then further refined in child classes.

check_cmdows_integrity(convention=True, mpg=None)

Method to check the integrity of the CMDOWS file that can be created with the save method.

Parameters:
  • convention (bool) – option for applying a UID convention
  • mpg (MdaoProcessGraph) – MPG to be saved together with graph
Returns:

check result
Return type:

bool

The integrity check is graph specific and thus needs to be executed for every graph before saving as CMDOWS if one wants to be sure that the CMDOWS file is integer. Due to its relative long runtime this check is however not performed automatically when using the save method.

check_for_coupling(function_order, only_feedback=False, raise_error_if_true=False)

Function to check for the presence of coupling in a graph for a list of analyses in a given analysis order.

Parameters:
  • function_order (list) – list with node names of functions
  • only_feedback (bool) – Boolean on whether to check for feedback coupling only (this is useful for Gauss-Seidel)
  • raise_error_if_true (bool) – Boolean on whether to raise an error if coupling exists
Returns:

Boolean value on whether coupling exists
Return type:

bool

Note

only the functions in the function_order list are checked for feedback.

copy_as(graph_class, as_view=False)

Method to make a copy of a graph and make it into another KADMOS graph class.

Returns:copy of the graph
Return type:KadmosGraph
copy_edge(old_edge, new_edge)

Method to copy an edge so that attributes of the old edge are maintained.

Parameters:
  • old_edge (tuple) – edge to be copied
  • new_edge (tuple) – edge to be created
Returns:

created edge
Return type:

tuple
copy_node_with_suffix(node, suffix, label_extension, **kwargs)

Method to simply copy a node and its attributes by creating a new node with a suffix.

Parameters:
  • node (str) – node to be copied
  • suffix (str) – suffix to be added to the node ID
  • label_extension (str) – extension for labels
  • kwargs (dict, int) – keyword arguments will be added as node attributes
Returns:

new node name and enriched graph
Return type:

str
count_function_nodes()

This function counts the amount function nodes that are present in the graph.

Returns:amount of function nodes counted in graph
Return type:int
create_dsm(file_name, destination_folder=None, open_pdf=False, mpg=None, include_system_vars=True, summarize_vars=False, function_order=None, keep_tex_file=False, abbreviate_keywords=False, compile_pdf=True, colors_based_on='problem_roles')

Method to create a (X)DSM PDF file

Parameters:
  • file_name (str) – name of the file to be saved
  • destination_folder (str) – destination folder for the file to be saved
  • open_pdf (bool) – option for opening the created file directly
  • mpg (MdaoProcessGraph) – optional MPG graph to be saved with MDG as XDSM (if None a DSM is created)
  • include_system_vars (bool) – option for including system variables (only applicable for DSMs)
  • summarize_vars (bool) – option for summarizing label
  • function_order (list) – optional function order for the diagonal of the graph (only applicable for DSMs)
  • keep_tex_file (bool) – optional argument to keep the tex file of the PDF
  • abbreviate_keywords (bool) – optional argument to keep make keywords shorter (input -> inp., output -> outp.)
  • compile_pdf (bool) – optional argument to compile the PDF
  • colors_based_on (str) – option to base the colors either on the problem role or the partitions
deepcopy()

Method to make a deep copy of a graph.

Returns:deepcopy of the graph
Return type:KadmosGraph
deepcopy_as(graph_class)

Method to make a deep copy of a graph and make it into another KADMOS graph class.

Returns:deepcopy of the graph
Return type:KadmosGraph
disconnect_problematic_variables_from(function, disconnect_collided_targets=True, disconnect_shared_sources=True, ignore_list=[])

Method to automatically disconnect certain problematic variables with respect to a given function.

Parameters:
  • function (basestring) – function around which problematic variables are disconnected
  • disconnect_collided_targets (bool) – setting to disconnect collided targets
  • disconnect_shared_sources (bool) – setting to disconnect shared sources
  • ignore_list (list) – setting to ignore certain nodes

If given as setting (disconnect_collided_targets=True) then the collided targets will be disconnected from this function. Also, if given as setting (disconnect_shared_sources=True), shared sources are also disconnected.

find_all_nodes(category='all', subcategory='all', attr_cond=None, attr_include=None, attr_exclude=None, xpath_include=None, xpath_exclude=None, print_in_log=False, print_attributes='all')

Advanced search function to get nodes and their properties.

Parameters:
  • category (str) – category of the node (you can specify multiple in a list), see note for allowed values.
  • subcategory (str) – subcategory of the node (you can specify multiple in a list), see note for allowed values.
  • attr_cond (list) – conditional on the node attribute value (e.g. [‘execution time’,’>’,200])
  • attr_include (list) – attributes to exclusively include in search
  • attr_exclude (list) – attributes to exclude from search
  • xpath_include (list) – xpaths to exclusively include in search
  • xpath_exclude (list) – xpaths to exclude from search
  • print_in_log (bool) – parameter to set printing in log on or off
  • print_attributes (str or list) – attributes that should be printed in the log
Returns:

list of all nodes that meet the search criteria
Return type:

list

Note

The following categories are allowed:

  • all
  • variable
  • variable group
  • function
  • architecture element
  • RCE component

Note

The following subcategories are allowed:

GROUPS:

  • all
  • all variables
  • all inputs
  • all outputs
  • all couplings
  • all circular variables
  • all PSG blocks
  • all iterative blocks
  • all design variables

VARIABLES:

  • hole
  • supplied input
  • supplied shared input
  • output
  • collision
  • coupling
  • pure circular coupling
  • shared coupling
  • shared circular coupling
  • collided coupling
  • collided circular coupling
  • collided shared coupling
  • collided shared circular coupling

VARIABLE GROUPS:

  • hole group
  • supplied input group
  • supplied shared input group
  • output group
  • coupling group
  • shared coupling group

FUNCTIONS:

  • hole
  • inputless
  • outputless
  • complete

ARCHITECTURE ELEMENTS:

Action blocks:

  • optimizer
  • MDA
  • optimizer function
  • MDA analysis
  • independent output function

Parameters:

  • initial guess design variable
  • final design variable
  • final output variable
  • MDA coupling variable
  • initial guess MDA coupling variable
  • final MDA coupling variable
  • consistency constraint variable

RCE COMPONENTS:

  • Input Provider
  • XML Merger
  • XML PyMerger
  • CPACS Tool
  • Converger
  • Optimizer
  • Consistency constraint function

Example usage: Just get all nodes of a graph in a list:

>>> all_nodes = self.find_all_nodes()

Get all input nodes in a list and print them in the log as well:

>>> all_nodes = self.find_all_nodes(category='all', subcategory='all inputs', print_in_log=True)

Get all input nodes with a certain attribute value:

>>> all_nodes = self.find_all_nodes(category='all', subcategory='all inputs',
>>>                                 attr_cond=['execution time', '>', 5], print_in_log=True)

Get all nodes with any of the listed attribute values:

>>> all_nodes = self.find_all_nodes(category='all', subcategory='all',
>>>                                 attr_include=[['problem_role', ['constraint', 'objective']],
>>>                                               ['instance', 1]])
get_adjacency_matrix(print_in_log=True)

Function to determine the adjacency matrix of a graph.

Parameters:print_in_log (bool) – option for printing the results
Returns:different methods of storing the same adjacency matrix in one dictionary
Return type:dict
get_architecture_node_ids(mdao_architecture, number_of_groups=None)

Method to get the IDs of architecture nodes specific for a certain MDAO architecture.

Parameters:
  • mdao_architecture (basestring) – specified architecture (CO, BLISS-2000, etc)
  • number_of_groups (int, None) – number of subgroups in distributed architectures
Returns:

node IDs
Return type:

tuple
get_architecture_node_labels(mdao_architecture, number_of_groups=None)

Method to get the labels of architecture nodes specific for a certain MDAO architecture.

Parameters:
  • mdao_architecture (basestring) – specified architecture (CO, BLISS-2000, etc)
  • number_of_groups (int, None) – number of subgroups in distributed architectures
Returns:

node labels
Return type:

tuple
get_categorized_nodes(print_in_log=False)

Function that returns a dictionary with graph nodes grouped according to category and subcategory.

Parameters:print_in_log (bool) – option for printing the categories
Returns:dictionary with analysis results
Return type:dict
get_contracted_graph(contraction_level)

This function contracts the nodes of a graph to the provided contraction level.

Parameters:contraction_level (int) – from 0 (highest level) to X (lowest level existing in XML schema)
Returns:contracted_graph: graph with contracted nodes

The contraction level refers to the xpath-level, which represents the position of the descendant with respect to its predecessors. The example below represents a level 3 node, with “cpacs” being at level zero.

/cpacs/aircraft/wings/wing

–cpacs

—-aircraft

——–wings

————wing

Note

All nodes above the contraction level are removed from the graph and replaced by a “variable group” node, which groups the removed nodes in a node at contraction level. This allows for a “de-cluttering” of the graph, with the graph connections still being represented.

get_direct_coupling_nodes(*args, **kwargs)

This method returns the direct couplings between two nodes in a graph.

Parameters:
  • args (str, list) – nodes to be checked for couplings; at least two must be provided
  • kwargs (direction: str) – print_couplings: option for printing all couplings with coupling direction and node (optional)
  • kwargs – direction: set only coupling in certain direction (optional)
Returns:

couplings: list of tuples containing the coupled nodes and the coupling node
Return type:

list

This method is specifically written (and tested) with function nodes in mind. Direct coupling is defined as coupling with between two nodes through a third node.

In this function, each combination between the provided arguments is tested for couplings (in pairs). First, the two nodes in each pair are checked for common neighbors. If they do, the edges of the common neighbors are iterated to determine whether the node-pair is connected to each other, or only the neighbor. The direction of the coupling is also checked.

Example:

  • The connection: F1 => N1 => F2
  • Leads to: [(F1, F2, N1)]
get_first_node_instance(node)

Method to obtain the first instance of a node

Parameters:node (str) – node to find first instance of
Returns:node of first instance
Return type:str
get_function_graph(keep_objective_variables=False)

Method for replacing variable nodes by function connections.

Param:keep_objective_variables: if given the objective variables will remain in graph
Type:keep_objective_variables: bool
Returns:new graph without variable nodes

Note

This function removes all variable nodes from the graph and replaces the variable connections of each function with function connections, such that if N(i) is a variable node, and F(i) a function node:

F(1) => N(1) => F(2) => N(2) => F(3)

becomes: F(1) => F(2) => F(3)

get_function_metadata(node)

Function to get the node metadata in a list (used in the dynamic visualization package).

Parameters:node (basestring) – function node to collect the metadata for.
Returns:list with metadata
Return type:list
get_function_nodes()

This function returns a list of all function nodes in the graph.

get_graph_properties(*args)

This function retrieves the properties of a graph.

Param:args: specific properties to be retrieved (optional)
Returns:dictionary containing the properties of the graph
Return type:dict

If no argument is given, the standard list of properties GRAPH_PROPERTIES is analyzed and their values are returned in a dict. If arguments are given, only this list will be used as standard list of properties.

get_node_attributes(node, attr_list, print_in_log=False)

Function to get and print certain attributes of a node from the graph.

Parameters:
  • node (str) – node name
  • attr_list (list) – list with attributes to be pulled
  • print_in_log (bool) – option for printing to log
Returns:

dictionary with node attributes
Return type:

dict
get_node_subcategory(node)

Method to analyse a node and to update the subcategory attribute of the node.

Parameters:node (basestring) – node in the graph
Returns:subcategory of the node
Return type:basestring

The following table illustrates how the subcategory is determined based on the category, indegree and outdegree:

NODE CATEGORY SUBCATEGORY INDEGREE OUTDEGREE
variable hole 0 0
supplied input 0 1
supplied shared input 0 >1
output 1 0
collision >1 0
coupling or pure circular coupling 1 1
shared coupling or shared circular coupling 1 >1
collided coupling or collided circular coupling >1 1
collided shared coupling or collided shared circular coupling >1 >1
function hole 0 0
inputless 0 >0
outputless >0 0
complete >0 >0
get_nodes_based_on_strings(*args, **kwargs)

This function enables the user to search graph nodes for specific strings.

Parameters:
  • args (str) – strings that graph nodes being searched for
  • kwargs (include_all: bool) – include_all: If include_all is set to True, all matching nodes are added to returned list
Returns:

matching nodes that user selected (all if include_all is True)

Each provided string will be searched for, and if multiple node are found for each string, the user will be able to select the ones desired. The other matched nodes are disregarded.

get_nodes_indegree()

Function to get the indegree of all the graph nodes and store them in a dictionary.

Returns:dictionary with node name key and indegree integer value.
Return type:dict
get_nodes_outdegree()

Function to get the outdegree of all the graph nodes and store them in a dictionary.

Returns:dictionary with node name key and outdegree integer value.
Return type:dict
get_nodes_subcategory()

Method to analyse all nodes and to update the subcategory attributes of the nodes.

get_number_of_couplings(node)

This function returns the number of couplings of a node in the graph.

Parameters:node – input node
Returns:number of couplings for the input node
Return type:int
get_same_graph_class(graph, copy_type='deep')

Method to reinstantiate a given graph according to the same graph class as the self.

Parameters:
  • graph (DiGraph) – graph object to be reinstantiated
  • copy_type (basestring) – setting to have a deep or shallow copy of the graph
Returns:

reinstantiated graph
Return type:

KadmosGraph
get_sources(node)

Function to determine the sources of a given node.

Parameters:node (basestring) – node for which sources should be found
Returns:list with sources
Return type:list
get_subgraph_by_function_nodes(*args, **kwargs)

This function retrieves a subgraph from the original graph only containing the argument nodes.

Parameters:
  • args (list, str) – arbitrary amount of graph nodes
  • kwargs (copy_type: str) – copy_type: type of copy (clean or deep)
Returns:

sub-graph only containing nodes provided as arguments, and their edges

Note

All arguments must be found in the graph.

get_system_inputs(*args, **kwargs)

This method checks whether there are system inputs in the graph using the function nodes provided.

Parameters:
  • args – function nodes
  • kwargs –
Returns:

system input nodes dictionary
Return type:

dict

Note

The function nodes should be provided in the args. If system inputs exist they are returned.

get_targets(node)

Function to determine the targets of a given node.

Parameters:node (basestring) – node for which targets should be found
Returns:list with targets
Return type:list
graph_has_nested_attributes(*attrbs)

Method to test if graph has nested attributes

Parameters:attrbs (str) – attributes to check for nesting in graph
Returns:presence of nested attribute
Return type:bool
has_nodes(nodes)

Function that checks whether all nodes given in a list are present in the graph.

Parameters:nodes (list) – list of nodes to be checked
Returns:boolean whether all nodes have been found
Return type:bool
inspect()

Method to print an overview of the graph.

Returns:printed overview of the graph
Return type:str
inspect_node(node)

Method to print a node with details.

Parameters:node (str) – node
Returns:printed node details
Return type:str
inspect_nodes(list_of_nodes)

Method to inspect/print a list of nodes with details.

Parameters:list_of_nodes (list) – node list
Returns:printed details of nodes
Return type:str
load_cmdows(cmdows, check_list, ignore_modes=False, keep_running=False)

Method to load the graph from a CMDOWS file

Parameters:
  • cmdows (str) – CMDOWS file path
  • check_list (list) – checks to be performed. See below.
  • ignore_modes (bool) – for determining if modes are taken into account
  • keep_running (bool) – for determining if errors should be raised
Returns:

mpg
Return type:

MdaoProcessGraph

Note

check_list options:

  • ‘consistent_root’: check if all in-/output files have the same root element
  • ‘invalid_leaf_elements’: check for leaf elements that still have child elements in other in-/output files
  • ‘schemas’: check if the in-/output files are consistent with the schema.

To perform all of the checks without stopping if an error is found by the checks:

>>> check_list = ['consistent_root', 'invalid_leaf_elements', 'schemas']
>>> graph.load_cmdows(cmdows, check_list, keep_running=True)
make_all_variables_valid()

Method to analyze all variables in a graph and make any problematic variables valid.

Returns:graph with problematic variables removed.

Problematic variable are holes and splittables. Splittable variables are split and holes will simply be removed.

merge_function_modes(*args, **kwargs)

Function to contract certain modes of the same function.

Parameters:
  • args (str) – function (first arg) and then followed by modes to be contracted.
  • kwargs (dict or str) – new_label to specify new node label manually (optional)
Returns:

contracted graph
Return type:

KadmosGraph
merge_function_nodes_based_on_modes(merge_funcs=None)

This class method merges all execution modes of the same function into a single node.

Parameters:merge_funcs (list, tuple) – List of tuple of functions to merge. If empty (default), all functions are merged.
Returns:merged graph

Mainly used for illustration purposes since information on the execution modes gets lost. Functions must be present in graph.

merge_functions(*args, **kwargs)

Function to merge a collection of functions.

Parameters:
  • args (node_ids) – functions to be merged
  • kwargs (dict) – new_label to specify new node label manually (optional)
Returns:

graph with merged function
Return type:

KadmosGraph
merge_parallel_functions(*args, **kwargs)

Function to merge a list of functions

Parameters:
  • args (node_ids) – functions to be merged
  • kwargs (dict) – new_label to specify new node label manually (optional)
Returns:

graph with merged functions
Return type:

KadmosGraph
merge_sequential_functions(*args, **kwargs)

Function to merge a collection of functions.

Parameters:
  • args (node_ids) – functions to be merged in the given sequence
  • kwargs (str) – new_label to specify new node label manually (optional)
Returns:

graph with merged functions
Return type:

KadmosGraph

Attention

It is assumed that the merged functions are actually executed in the sequence in which they are given to this function.

node_is_function(node)

Function that checks whether a node is a function node or not.

Parameters:node (str) – node in graph
Returns:check result
Return type:bool
node_is_hole(node)

Function that checks whether a node is a hole (unconnected).

Parameters:node (str) – node in graph
Returns:check result
Return type:bool
node_is_objective_function(node)

This function checks whether the provided node is a objective function.

Parameters:node (str) – graph node to be tested
Returns:check result
Return type:bool
This function checks whether the provided node:
  1. exists in graph
  2. has name-attribute “Objective”
  3. has an out-degree of 1
  4. has an outgoing node with an out-degree of zero

Note

Only if all checks are satisfied, is the node a valid objective function node.

node_is_output(node)

Function that checks whether a node is a system output.

Parameters:node (str) – node in graph
Returns:check result
Return type:bool
node_is_variable(node)

Function that checks whether a node is a variable node or not.

Parameters:node (str) – node in graph
Returns:check result
Return type:bool
plot_adjacency_matrix(fig_num=1, fig_size=(7, 7), show_now=True)

Draw the adjacency matrix in a plot window.

Parameters:
  • fig_num – figure number
  • fig_size – figure size
  • show_now – Boolean whether to plot directly (pausing the execution until the plot is closed), or not.
plot_graph(fig_num=1, color_setting='default', positioning='circular', save_as=None, show_now=True, title=None, edge_label=False)

Function to plot a graph.

Parameters:
  • fig_num (int) – figure number
  • color_setting (str) – choose from ‘default’, ‘sinks’, ‘categories’, ‘partitions’
  • positioning (str) – choose from ‘circular’, ‘spring’
  • save_as (bool) – save plot as figure file
  • show_now (bool) – Boolean whether to plot directly (pausing the execution until the plot is closed), or not.
  • title (str) – title of the graph
  • edge_label (str) – edge attribute that will be shown for each edge in graph
Returns:

window with plot

Hint

if the plot window is not showing, you need to add matplotlib.pyplot.show() at the end of your code

print_graph(use_pretty_print=False)

Function to print the full graph.

Parameters:use_pretty_print (bool) – Boolean on whether to use pretty print for node and edge attributes
relabel_function_nodes(mapping=None)

Method to relabel all function nodes so that they meet the minimum CMDOWS convention

Parameters:mapping (None, bool) – application of mapping required, optional (default=None)
Returns:relabeled graph

CMDOWS convention:

  • Minimum information: ID
  • Maximum information: ID[modeID][instanceID][version]

Note

modeID, instanceID and version are only provided if there is a function with the same ID that requires this specification to differentiate between the functions.

Example:

Design competences (full information):

  1. Aerodynamics[A][1][v1]
  2. Aerodynamics[A][2][v1]
  3. Aerodynamics[B][1][v1]
  4. Structures[A][1][v1]
  5. Structures[A}[1][v2]
  6. Propulsion[A][1][v2]

Design competences relabeled:

  1. Aerodynamics[A][1]
  2. Aerodynamics[A][2]
  3. Aerodynamics[B][1]
  4. Structure[v1]
  5. Structure[v2]
  6. Propulsion
remove_function_nodes(*args)

Function that removes a function node

Parameters:args (str, list) – function node id(s)
Returns:graph with nodes removed
Return type:KadmosGraph

Attention

Also the input and output variables that only have links to this specific function node are deleted. A simple remove_node of a function node might lead to unconnected variables in the graph.

save(file_name, file_type='kdms', graph_check_critical=True, destination_folder=None, mpg=None, description='', creator='', version='1.0', timestamp=datetime.datetime(2018, 4, 21, 16, 47, 29, 483000), keep_empty_elements=False, pretty_print=False, convention=True, integrity=False)

Method to save the graph.

Parameters:
  • file_name (str) – name of the file to be saved
  • file_type (str) – file_type
  • graph_check_critical (bool) – option for raising errors in case of an invalid graph
  • destination_folder (str) – destination folder for the file to be saved
  • mpg (MdaoProcessGraph) – optional MPG graph to be saved with MDG
  • description (str) – description of the file (only applicable for the cmdows file type)
  • creator (str) – name of the creator of the file (only applicable for the cmdows file type)
  • version (str, float, int) – version of the file (only applicable for the cmdows file type)
  • timestamp (datetime) – timestamp to be saved in the file (only applicable for the cmdows file type)
  • keep_empty_elements (bool) – option for keeping empty XML elements (only applicable for the cmdows file type)
  • pretty_print (bool) – option for pretty XML printing (only applicable for the cmdows file type)
  • convention (bool) – option for appyling a UID convention (only applicable for the cmdows file type)
  • integrity (bool) – option for doing an integrity file check (only applicable for the cmdows file type)

Note

Different output file types are implemented for saving graphs. They are listed below:

  • kdms: the most simple file type which makes use of pickling
  • cmdows: the most versatile file type especially suited for file exchange with other tools
  • graphml: another file type especially suited for file exchange with other tools based on graphs
select_objectives_from_graph(*args)

This functions lets the user select one or more objective functions from the graph.

Parameters:args (str) – objective functions to choose from
Returns:list of objective functions
Return type:list

Note

Only functions can be selected as objectives. If no arguments are provided, user is prompted to select an objective from all functions in graph.

split_variables(*args, **kwargs)

Method to split a problematic variable node into multiple separate valid nodes.

Parameters:
  • args (basestring, list) – problematic node in the graph
  • kwargs –
Returns:

graph with split variables
Return type:

KadmosGraph

The following variables are considered problematic and will be handled by this function:

  • pure circular coupling
  • shared circular coupling
  • collided coupling
  • collision
  • collided circular coupling
  • collided shared coupling
  • collided shared circular coupling

The following table shows an example situation for each of the different problematic variables:

PROBLEMATIC VARIABLE SITUATION FUNCTION ORDER RESOLVED AS
pure circular coupling x1 <=> F1 n.a. x1/variableInstance1 => F1 => x1/variableInstance2
shared circular coupling F1 <=> x1 => F2, F3 n.a.
x1/variableInstance1 => F1 =>
x1/variableInstance2 => F2, F3, etc.
collided (shared) coupling F1, F3 => x1 => F2, F4 F1, F2, F3, F4 F1 => x1/variableInstance1 => F2
F3 => x1/variableInstance2 => F4
collision F1, F2 => x1 F1, F2 F1 => x1/variableInstance1
F2 => x1/variableInstance2
collided circular coupling F1 <=> x1 <= F2, F3 n.a. x1/variableInstance1 => F1 => x1/variableInstance2
F2 => x1/variableInstance3
F3 => x1/variableInstance4
collided shared circular coupling F3, F5 <=> x1 <= F2 x1 => F1, F4, F6 F1, F2, F3, F4, F5, F6 x1/variableInstance1 => F2 => (..)
x1/variableInstance2 => F1, F3 => (..)
x1/variableInstance3 => F4, F5 => (..)
x1/variableInstance4 => F6

DataGraph

class kadmos.graph.graph_data.DataGraph(*args, **kwargs)
get_coupling_dictionary()

Function to get a coupling dictionary.

Returns:coupling dictionary
Return type:dict

For each function node, the dictionary indicates from which function nodes it gets its input and the number of variables it gets.

  • F2 ==> x1, x2 ==> F1
  • F3 ==> x3 ==> F1

Will give: {F1: {F2: 2, F3: 1}}

get_coupling_matrix(function_order_method='manual', node_selection=None)

Function to determine the role of the different functions in the FPG.

Parameters:
  • function_order_method (basestring) – algorithm to be used for the order in which the functions are executed.
  • node_selection (list) – selection of nodes for which the coupling matrix will be calculated only
Returns:

graph with enriched function node attributes and function problem role dictionary
Return type:

FundamentalProblemGraph
get_feedback_info(function_order, coupling_dict)

Function to determine the number of feedback loops for a given function order

Parameters:
  • function_order (list) – function order of the nodes
  • coupling_dict (dict) – function couplings
Returns:

number of feedback loops
Return type:

int
get_highest_instance(node)

Method to get the highest instance of a node.

Parameters:node (str) – node
Returns:highest instance of the node
Return type:int
get_possible_function_order(method, multi_start=None, check_graph=False)

Method to find a possible function order, in the order: pre-coupled, coupled, post-coupled functions

Parameters:
  • method (str) – algorithm which will be used to minimize the feedback loops
  • multi_start (int) – start the algorithm from multiple starting points
  • check_graph (bool) – check whether graph has problematic variables
Returns:

Possible function order
Return type:

list
mark_as_constraint(node, operator, reference_value, remove_unused_outputs=False)

Method to mark a node as a constraint.

Parameters:
  • node (str) – node to be marked (on the left side of the operator
  • operator (str or string list) – constraint operator or list of constraint operators
  • reference_value (numbers.Number or list) – value on the right side of the operator or list of values corresponding to the list of operators
  • remove_unused_outputs (bool) – option to remove unused outputs
Returns:

graph with enriched constraint node
mark_as_constraints(nodes, operators, reference_values, remove_unused_outputs=False)

Method to mark multiple nodes as constraints.

Parameters:
  • nodes (list) – nodes to be marked.
  • operators (str, list) – operators to be implemented (as list per node or as single operator for all)
  • reference_values (float, list) – reference values to be used (as list of values per node or as single value for all)
  • remove_unused_outputs (bool) – option to remove unused outputs
Returns:

graph with enriched constraint nodes

Operators: ‘==’, ‘>’, ‘<’, ‘>=’ and ‘<=’

mark_as_design_variable(node, lower_bound=None, upper_bound=None, samples=None, nominal_value=0.0, ignore_outdegree=False)

Method to mark a single node as a design variable and add the required metadata for its definition.

Parameters:
  • node (str) – node
  • lower_bound (float) – lower bound of design variable
  • upper_bound (float) – upper bound of design variable
  • samples (list) – samples of design variable
  • nominal_value (float) – nominal value of design variable
  • ignore_outdegree (bool) – option to ignore the outdegree required
Returns:

graph with enriched design variable node
mark_as_design_variables(nodes, lower_bounds=None, upper_bounds=None, samples=None, nominal_values=None)

Method to mark a list of nodes as design variable and add metadata.

Parameters:
  • nodes (list or str) – list of nodes present in the graph
  • lower_bounds (list, numbers.Number) – list of lower bound values
  • upper_bounds (list, numbers.Number) – list of upper bounds
  • samples (list) – nested list of kadmos values
  • nominal_values (list, numbers.Number) – list of nominal values
Returns:

graph with enriched design variable nodes
mark_as_objective(node, remove_unused_outputs=False)

Method to mark a single node as objective.

Parameters:
  • node (basestring) – variable node
  • remove_unused_outputs (bool) – option to remove unused outputs
Returns:

graph enriched with objective node
mark_as_qois(nodes, remove_unused_outputs=False)

Function to mark a list of nodes as quantity of interest.

Parameters:
  • nodes (list) – list of nodes present in the graph
  • remove_unused_outputs (bool) – option to remove unused outputs
minimize_feedback(nodes, method, multi_start=None, get_evaluations=False)

Function to find the function order with minimum feedback

Parameters:
  • nodes (list) – nodes for which the feedback needs to be minimized
  • method (str) – algorithm used to find optimal function order
  • multi_start (int) – start the algorithm from multiple starting points
  • get_evaluations (bool) – option to give the number of evaluations as output
Returns:

function order
Return type:

list
Returns:

number of evaluations
Return type:

list
remove_unused_outputs()

Function to remove output nodes from an FPG which do not have a problem role.

Returns:the nodes that were removed
Return type:list
sort_nodes_for_process(nodes)

Method to sort function nodes such that the process order is optimized, while not increasing the number of feedback loops

Parameters:nodes (list) – function nodes to sort
Returns:nodes in sorted order
Return type:list
unmark_variable(node)

Function to unmark any marked variable.

Parameters:node (basestring) – variable node to be unmarked
Returns:graph with unmarked node

RepositoryConnectivityGraph

class kadmos.graph.graph_data.RepositoryConnectivityGraph(*args, **kwargs)
create_mathematical_problem(n_disciplines, coupling_strength=None, n_global_var=None, n_local_var=None, n_coupling_var=None, n_local_constraints=None, n_global_constraints=None, B=None, C=None, D=None, E=None, F=None, G=None, H=None, I=None, J=None, r=None, s=None, write_problem_to_textfile=False)

Function to get a mathematical problem according to the variable complexity problem as described in: Zhang D., Song B., Wang P. and He Y. ‘Performance Evaluation of MDO Architectures within a Variable Complexity Problem’, Mathematical Problems in Engineering, 2017.

Parameters:
  • n_disciplines (int) – Number of disciplines
  • coupling_strength (int) – percentage of couplings, 0 no couplings, 1 all possible couplings
  • n_global_var (int) – Number of global design variables
  • n_local_var (int) – Number of local design variables for each discipline
  • n_coupling_var (int) – Number of output variables for each discipline
  • n_local_constraints (int) – Number of local constraints
  • n_global_constraints (int) – Number of global constraints
  • B – relation between the coupling variables
  • C – relation between the global design variables and coupling variables
  • D – relation between the local design variables and coupling variables
  • E – relation between the global design variables and local constraints
  • F – relation between the local design variables and local constraints
  • G – relation between the coupling variables and local constraints
  • H – relation between the global design variables and global constraints
  • I – relation between the local design variables and global constraints
  • J – relation between the coupling variables and global constraints
  • r (float) – positive scalars to be used for the local constraints
  • s (float) – positive scalars to be used for the global constraints
  • write_problem_to_textfile (bool) – option to write generated problem to a textfile
get_fpg_based_on_function_nodes(*args, **kwargs)

Function to get the Fundamental Problem Graph based on a list of (or a single) function.

Parameters:
  • args (str) – node names of functions of interest
  • kwargs (name: str) – name: name of the graph to be generated
Returns:

Fundamental Problem Graph object
Return type:

FundamentalProblemGraph
get_fpg_based_on_list_functions(list_of_functions, name='FPG')

Function to get a Fundamental Problem Graph based on a list of functions.

Parameters:
  • list_of_functions (list) – list with strings that specify the desired functions
  • name (str) – name of the graph to be generated
Returns:

Fundamental Problem Graph object
Return type:

FundamentalProblemGraph
get_fpg_based_on_sinks(list_of_sinks, name='FPG')

Function to get the a Fundamental Problem Graph based on a list of sinks/required output variables.

Parameters:
  • list_of_sinks (list) – list with strings that specify the desired output
  • name (str) – name of the graph to be generated
Returns:

Fundamental Problem Graph object
Return type:

FundamentalProblemGraph
get_fpg_by_function_nodes(*args)

This function creates a new (FPG)-graph based on the selected function nodes.

Parameters:args (list, str) – arbitrary amount of graph nodes
Returns:new fpg graph
Return type:FundamentalProblemGraph
get_function_paths_by_objective(*args, **kwargs)

This function takes an arbitrary amount of objective nodes as graph sinks and returns all path combinations of tools.

Parameters:
  • args (list, str) – arbitrary amount of objective nodes
  • kwargs (bool, str) – filter options to limit possible path combinations
Returns:

all possible FPG path combinations for the provided objective nodes

If no arguments are given, user is prompted to select objectives from the graph.

Hint

The tool combinations are found using the function itertools.product() and can lead to significant computation times for large graphs. If this is the case, the user is prompted whether to continue or not.

A variety of filters can be applied to the search of possible tools combinations, some of which reduce the computation time.

Note

kwargs:

  • obj_vars_covered - ensures that all objective variables are used in tool configurations
  • ignore_funcs - ignores functions for the config
  • source - source node; if provided, must be in config
get_path_combinations(*args, **kwargs)

This function takes lists of subsets and generates all possible combinations between them.

Parameters:
  • args (list) – lists of subsets that will be used to find configurations
  • kwargs (int) – see optional arguments
Returns:

set of all unique path combinations

This is done by using the itertools.product() function. If the amount of expected evaluations exceeds a pre-set minimum, the user will be asked if to continue or not; because the process can take a long time and use up many resources.

Note

Optional arguments:

  • min_func: minimum amount of functions in each configuration
  • max_func: maximum amount of functions in each configuration
select_function_combination_from(*args, **kwargs)

This function takes all provided workflow configurations and lists them according to their characteristics.

Parameters:
  • args (list) – workflow configurations
  • kwargs (bool, str, int) – see optional arguments
Returns:

sorted list of workflow configurations
Return type:

list

Note

Optional arguments:

  • ‘print_combos’ - option to print the combinations in a table
  • ‘n_limit’ - amount of combinations that will be printed in the table
  • ‘sort_by’ - characteristic to sort workflow configurations by
  • ‘sort_by ascending’ - option to sort workflow configurations by ascension
  • ‘plot_combos’ - option to plot the combinations in a graph

The user can choose the workflow configuration from the list.

A warning is given to the user if the amount of total configurations exceeds n = 1e4. Print limit is set to [0-20] by default.

Note

sort_by must be one of [“couplings”, “system_inputs”, “edges”, “nodes”].

FundamentalProblemGraph

class kadmos.graph.graph_data.FundamentalProblemGraph(*args, **kwargs)
add_function_problem_roles(function_order_method='manual')

Method to add the function problem roles (pre-coupled, coupled, post-coupled functions).

Parameters:function_order_method (basestring) – algorithm to be used for the order in which the functions are executed.
Returns:graph with problem roles added to functions
Return type:FundamentalProblemGraph

Algorithm options:

  • ‘manual’ - function order attributed to the problem formulation of the graph is used
add_problem_formulation(mdao_definition, function_order, doe_settings=None)

Method to add the problem formulation.

Parameters:
  • mdao_definition (str) – MDF-GS, MDF-J, IDF, CO, BLIS-2000
  • function_order (list) – order or functions to be included in graph
  • doe_settings (dict) – doe settings of the graph
Returns:

graph enriched with problem formulation
Return type:

Fundamental ProblemGraph
check_and_get_coupled_functions_groups(coupled_functions_groups=None)

Method to obtain the coupled functions and check them if provided

Parameters:coupling_functions_groups (list) – (optional) coupled function groups
Returns:coupled function groups
Return type:list

Checks whether the function is a node of the graph with the problem role ‘coupled’

check_and_get_design_variables(des_vars=None)

Method to obtain the design variable nodes and check them if provided

Parameters:des_vars (list) – (optional) design variable nodes
Returns:design variable nodes
Return type:list

Checks whether the function is a node of the graph with the problem role ‘design variable’

check_and_get_post_coupling_functions(post_coupling_functions=None)

Method to obtain the post-coupled functions and check them if provided

Parameters:post_coupling_functions (list) – (optional) post-coupled function nodes
Returns:post-coupled function nodes
Return type:list

Checks whether the function is a node of the graph with the problem role ‘post-coupled’

check_and_get_pre_coupling_functions(pre_coupling_functions=None)

Method to obtain the pre-coupled functions and check them if provided

Parameters:pre_coupling_functions (list) – (optional) pre-coupled function nodes
Returns:pre-coupled function nodes
Return type:list

Checks whether the function is a node of the graph with the problem role ‘pre-coupled’

create_mdg(mg_function_ordering, name='MDG')

Function to automatically create an MDG based on an FPG.

Parameters:
  • mg_function_ordering (dict) – dictionary with MDAO graph function ordering
  • name (basestring) – name for the MDG graph
Returns:

baseline MDG (only added additional action blocks, no changed connections)
Return type:

MdaoDataGraph
create_mpg(mg_function_ordering, name='MPG')

Function to automatically create a MPG based on a FPG.

Parameters:
  • mg_function_ordering (dict) – dictionary with MDAO graph function ordering
  • name (basestring) – name for the MPG graph
Returns:

unconnected FPG (only action blocks and their diagonal position)
Return type:

MdaoProcessGraph
determine_scope_constraint_functions(cnstrnt_vars=None, coupled_functions_groups=None, post_coupling_functions=None)

Method to determine the scope (global, local) of the constraint functions based on the constraint variables and to determine on which coupled function groups the constraint function depends.

Parameters:
  • cnstrnt_vars (list) – (optional) constraint variables to be determined
  • coupled_functions_groups (list) – (optional) list of lists with coupled functions groups
  • post_coupling_functions (list) – (optional) list with post-coupling functions
Returns:

global constraint variables and functions, local constraint variables and functions, groups indices per constraint function
Return type:

tuple
determine_scope_design_variables(des_vars=None, coupled_functions_groups=None, pre_coupling_functions=None)

Method to determine the scope (global, local) of the design variables and to determine to which coupled function groups the design variable belongs.

Parameters:
  • des_vars (list) – list of design variables (if not given, it is taken from the graph)
  • coupled_functions_groups (list) – list with list of coupled function groups
  • pre_coupling_functions (list) – list with list of pre-coupled function groups
Returns:

list of global design variables, list of local design variables, dictionary with dependencies
Return type:

tuple
get_group_couplings(coupled_functions_groups=None)

Method to obtain group couplings and their indices.

Parameters:coupled_functions_groups (list) – (optional) list of coupled function groups
Returns:group couplings present
Return type:list
Returns:index of coupled groups
Return type:dict
get_mdg(name='MDG')

Create the MDAO data graph for a given FPG.

Parameters:name (basestring) – name of the new graph
Returns:MDAO data graph
Return type:MdaoDataGraph
get_mg_function_ordering()

Method to determine the function ordering for MDAO graphs (FPG and MDG) based on an FPG.

Returns:function ordering dictionary
Return type:dict

Note

Function ordering has to be adjusted when design variables are used. In that case, the pre-coupling functions have to be divided in two parts: the first part does not use the design variables yet, while the second does.

get_objective_node()

Method to get the single (or non-existent) objective node from a graph.

Returns:objective node or None if no objective node is present
Return type:str, None
get_partition_info(partitions, include_run_time=False)

Function to get the number of feedback variables in the partitions and the number system variables (feedback and feedforward variables between partitions)

Parameters:
  • partitions (dict) – dictionary which indicates which nodes are in which partition
  • include_run_time (bool) –
Returns:

partition_variables:
Return type:

partition_variables: list of sets
Returns:

system_variables:
Return type:

system_variables: set
get_sub_level_functions(local_objective_function, local_cnstrnt_funcs, coupled_functions_group, mg_function_ordering=None)

Method to obtain subsystem level functions.

Parameters:
  • local_objective_function (str) – local objective function
  • local_cnstrnt_funcs (list) – local constraint function(s)
  • coupled_functions_group (list) – coupled functions
  • mg_function_ordering (dict) – (optional) MdaoGraph function ordering
Returns:

subsystem level functions
Return type:

dict
get_sys_post_couplings(sys_level_post_coupled, coupled_functions_groups=None)

Method to obtain the system-level post-couplings functions.

Parameters:
  • sys_level_post_coupled (list) – nodes with attributed problem role ‘post-coupling’
  • coupled_functions_groups (list) – (optional) list of coupled function groups
Returns:

system-level post-coupling functions
Return type:

list
Returns:

indices ot system-level post-coupling functions
Return type:

dict
get_system_level_functions(global_objective_function, global_cnstrnt_functions, mg_function_ordering=None)

Method to obtain system level functions

Parameters:
  • global_objective_function (str) – global objective function
  • global_cnstrnt_functions (list) – global constraint function(s)
  • mg_function_ordering (dict) – MdaoGraph function ordering
Returns:

system level functions
Return type:

dict
impose_mdao_architecture()

Method to directly get both the MDG and MPG of an FPG.

Returns:MdaoDataGraph and MdaoProcessGraph
Return type:tuple
partition_graph(n_parts, include_run_time=False, tpwgts=None, recursive=True, contig=False)

Partition a graph using the Metis algorithm (http://glaros.dtc.umn.edu/gkhome/metis/metis/overview).

Parameters:
  • n_parts (int) – number of partitions requested (algorithm might provide less)
  • include_run_time (bool) –
  • tpwgts (list) – list of target partition weights
  • recursive (bool) – option to use the recursive bisection or k-way partitioning algorithm
  • contig (bool) – Metis option

Note

partitioning can only be performed on undirected graphs. Therefore every graph input is translated into an undirected graph.

select_distributed_architecture()

Function for easy selection of a distributed architecture for a partitioned graph.

Returns:Extended problem formulation
select_number_of_partitions(partition_range, include_run_time=False, plot_pareto_front=False)

Function to evaluate the properties of different number of partitions and to select the best one.

Parameters:
  • partition_range (list) – range of number of partitions that need to be evaluated
  • include_run_time –
  • plot_pareto_front (bool) – Option to plot the characteristics of different number of partitions
Returns:

MdaoProcessGraph

class kadmos.graph.graph_process.MdaoProcessGraph(*args, **kwargs)
add_process(sequence, start_step, mdg, end_in_iterative_node=None)

Method to add a process to a list of functions.

The sequence is assumed to be the order of the functions in the input list. The sequence is considered simple, since it is not analyzed for the possibility to run functions in parallel.

Parameters:
  • sequence (list) – list of functions in the required sequence
  • start_step (int) – process step number for the first element in the sequence
  • mdg (MdaoDataGraph) – data graph to be used for execution dependencies
  • end_in_iterative_node (basestring) – (optional) iterative node to which the last function should go
add_process_partitions(previous_sequence, partitions, next_sequence, process_step, mdg, end_in_iterative_node=None)

Function to add the process in the partitions

Parameters:
  • previous_sequence (list) – previous list of functions in the required sequence
  • partitions (dict) – process partitions to be added
  • next_sequence (list) – next list of functions in the required sequence
  • process_step (int) – process step number for the first element in the sequence
  • mdg (MdaoDataGraph) – data graph to be used for execution dependencies
  • end_in_iterative_node (basestring) – (optional) iterative node to which the last function should go
Returns:

graph enriched with process partitioning
Return type:

MdaoProcessGraph
connect_nested_iterators(master, slave, direction='slave->master')

Method to connect a slave iterator to a master iterator in a nested configuration.

Parameters:
  • master (basestring) – upper iterator node in the nested configuration
  • slave (basestring) – lower iterator node in the nested configuration
Returns:

graph enriched with nested iterators
Return type:

MdaoProcessGraph

An example is if a converger inside an optimizer in MDF needs to be linked back.

get_lowest_psn(cycle)

Method to retrieve the lowest process step number (PSN) of a list of nodes in a cycle.

Parameters:cycle (list) – list with nodes on a cycle
Returns:the minimal PSN and the index of the first element having this PSN
Return type:tuple
get_node_text(node)

Method to determine the text of a function node (for use in a XDSM diagram).

Parameters:node (basestring) – node
Returns:node text for in the XDSM function box
Return type:basestring
get_ordered_cycles()

Method to get the cycles of a process graph ordered according to process step number.

Returns:list with the cycles of a graph ordered based on PSN
Return type:list
get_process_hierarchy()

Method to assess the hierarchy of the process based on the process lines in a ProcessGraph.

Returns:nested list with process hierarchy, e.g. [COOR, A, [OPT, [CONV, D1, D2], F1, G1, G2]]
Return type:list
get_process_list(use_d3js_node_ids=False)

Method to get the xdsm workflow process list (for use in dynamic visualizations).

Parameters:use_d3js_node_ids (bool) – setting whether node names should be changed into node ids according to D3js notation.
Returns:process list
Return type:list
inspect_process()

Method to print the MPG.

Returns:printed inspection

MdaoMixin

class kadmos.graph.mixin_mdao.MdaoMixin
insert_node_on_diagonal(node, diagonal_pos, attr_dict=None)

Function to insert a node on the diagonal with functions.

Parameters:
  • node (str) – node identifier
  • diagonal_pos (int) – integer with diagonal position (>= 0)
  • attr_dict (dict) – dictionary with node attributes
Returns:

graph with inserted node on diagonal
Return type:

MdaoMixin
remove_node_from_diagonal(node)

Function to remove a node from the diagonal with functions.

Parameters:node (str) – node identifier
Returns:graph with removed node from diagonal
Return type:MdaoMixin

EquationMixin

class kadmos.graph.mixin_equation.EquationMixin
add_equation(edge_or_node, equation, language='Python')

Method to add an equation to an output edge.

Parameters:
  • edge_or_node (list, str) – graph edge or node under consideration.
  • equation (str) – equation to be added
  • language (str) – equation language used for the equation
Returns:

equation
Return type:

str
add_equation_label(edge, labeling_method='node_label', language='Python')

Method to add an equation label to a edge that can (safely) be used as reference in an equation.

Parameters:
  • edge (str) – graph edge under consideration
  • labeling_method (str) – select method for automatic label string determination (node_id or node_label)
  • language (str) – equation language used for the equation label
Returns:

label
Return type:

str
add_equation_labels(nodes, language='Python', labeling_method='node_id')

Method to add equation labels automatically to all input edges connected to the specified list of nodes

Parameters:
  • nodes (list) – list of nodes
  • language (str) – equation language used for the equation label
  • labeling_method (str) – select method for automatic label string determination (node_id or node_label)
Returns:

labels
Return type:

str
add_mathematical_function(input_nodes, function_node, output_nodes, function_type='regular')

Method to add mathematical function to graph

Parameters:
  • input_nodes (str, list) – input nodes of the mathematical function
  • function_node (str) – function node of the mathematical function
  • output_nodes (str, list) – output nodes of the mathematical function
  • function_type ('regular' or 'consistency') – type of function, optional (default=’regular’)
Returns:

mathematical function
Return type:

str

VistomsMixin

class kadmos.graph.mixin_vistoms.VistomsMixin
vistoms_add(vistoms_dir, mpg=None, function_order=None, reference_file=None, compress=False, remove_after_compress=True, graph_id=None, replacement_id=None, xml_file=None)

Function to add a graph to a existing VISTOMS instance.

Parameters:
  • vistoms_dir (str) – directory of the VISTOMS directory to be used for addition
  • mpg (MdaoProcessGraph) – optional MPG graph to be saved with MDG as XDSM (if None a DSM is created)
  • function_order (list) – optional function order for the diagonal of the graph (only applicable for DSMs)
  • reference_file (str) – file from which reference values are extracted (either full path or file in same folder)
  • compress (bool) – setting whether to compress the final VISTOMS instance folder to a zip file
  • remove_after_compress (bool) – setting whether to remove the original folder after compression
  • graph_id (basestring) – indentifier of the new graph
  • replacement_id (basestring) – indentifier of the graph to be replaced
  • xml_file (file) – Name of the CMDOWS xml-file
Returns:

enriched vistoms instance
Return type:

VistomsMixin

Hint

In one VISTOMS instance different graphs can be shown. For example it is possible to include different architectures for the same problem in one VISTOMS instance.

vistoms_add_json(mpg=None, function_order=None, graph_id=None)

Function to add a graph to a existing VISTOMS instance.

In one VISTOMS instance different graphs can be shown. For example it is possible to include different architectures for the same problem in one VISTOMS instance.

Parameters:
  • mpg (MdaoProcessGraph) – optional MPG graph to be saved with MDG as XDSM (if None a DSM is created)
  • function_order (list) – optional function order for the diagonal of the graph (only applicable for DSMs)
vistoms_create(vistoms_dir, vistoms_version=None, mpg=None, function_order=None, reference_file=None, compress=False, remove_after_compress=True, graph_id=None, use_png_figs=False, file_refs=None, xml_file=None)

Function to create a new VISTOMS instance from a graph.

Parameters:
  • vistoms_dir (str) – directory of the VISTOMS directory to be created
  • vistoms_version – version of the VISTOMS instance to be used (as stored in the package itself)
  • vistoms_version – str
  • mpg (MdaoProcessGraph) – optional MPG graph to be saved with MDG as XDSM (if None a DSM is created)
  • function_order (list) – optional function order for the diagonal of the graph (only applicable for DSMs)
  • reference_file (str) – file from which reference values are extracted (either full path or file in same folder)
  • compress (bool) – setting whether to compress the final VISTOMS instance folder to a zip file
  • remove_after_compress (bool) – setting whether to remove the original folder after compression
  • graph_id (basestring) – identifier of the new graph
  • use_png_figs (bool) – setting whether to use the PNG figures instead of the SVG figures for local execution
  • file_refs (dict) – setting to provide file references manually (to use VISTOMS on a server)
  • xml_file (file) – Name of the CMDOWS xml file
Returns:

vistoms instance
Return type:

VistomsMixin
vistoms_start(file_dir=None, mpg=None)

Function to open an interactive VISTOMS based on the data graph and (optionally) the MPG. If file_dir is not provided then the files are stored in a temp directory.

Parameters:
  • file_dir (path) – folder name or path where the graphs used in the interactive VISTOMS will be stored.
  • mpg (MdaoProcessGraph) – MDAO process graph to be used in combination with the data graph.
Returns:

interactive VISTOMS
Return type:

file

CMDOWS

Below the CMDOWS file operations library is listed.

class kadmos.cmdows.cmdows.CMDOWS(file_path=None, element=None)

Class for with various methods for checking and manipulating CMDOWS files

add_actor(contact_uid, role)

Method to add a role element to the organization branch.

Parameters:
  • contact_uid (basestring) – uID of the contact
  • role (str) – contact role
Returns:

enriched organizational branch
Return type:

XMLSchema

Note

Role options are:

  • ‘architect’
  • ‘integrator’
  • ‘collaborative_engineer’
  • ‘tool_specialist’
  • ‘customer’
add_contact(name, email, uid, company=None, department=None, function=None, address=None, telephone=None, country=None, roles=None)

Method to add a contact element to the organization branch.

Parameters:
  • name (str) – contact name
  • email (str) – contact email
  • uid (basestring) – contact uID
  • company (str) – (optional) contact company
  • department (str) – (optional) company department
  • function (str) – (optional) contact function
  • address (str) – (optional) contact address
  • telephone (str) – (optional) contact telephone number
  • country (str) – (optional) contact country
  • roles (list, str) – contact roles in project
Returns:

XML with contact
Return type:

XMLSchema

Note

Role options are:

  • ‘architect’
  • ‘integrator’
  • ‘collaborative_engineer’
  • ‘tool_specialist’
  • ‘customer’
add_dc(uid, id, mode_id, instance_id, version, label)

Method to add a designCompetence element to the designCompetences branch.

Parameters:
  • uid (basestring) – designCompetence uID
  • id (str) – designCompetence ID
  • mode_id (str) – designCompetence mode
  • instance_id (int) – designCompetence instance
  • version (str) – designCompetence instance
  • label (str) – designCompetence label
Returns:

XML with designCompetence
Return type:

XMLSchema
add_dc_general_info(dc_uid, description, status=None, creation_date=None, owner_uid=None, creator_uid=None, operator_uid=None, model_definition=None)

Method to add a general info element to a DC branch.

Parameters:
  • dc_uid (basestring) – designCompetence uID
  • description (str) – designCompetence description
  • status (str) – (optional) designCompetence status
  • creation_date (datetime) – (optional) designCompetence creation date
  • owner_uid (basestring) – (optional) designCompetence owner uID
  • creator_uid (basestring) – (optional) designCompetence creator uID
  • operator_uid (basestring) – (optional) designCompetence operator uID
  • model_definition (str) – (optional) designCompetence model definition
Returns:

XML with general info
Return type:

XMLSchema
add_dc_inputs_element(dc_uid, inputs_element)

Method to add a input element to a DC element.

Parameters:
  • dc_uid (basestring) – designCompetence uID
  • inputs_element (str) – input element to be added
Returns:

XML with input element
Return type:

XMLSchema
add_dc_outputs_element(dc_uid, outputs_element)

Method to add a output element to a DC element.

Parameters:
  • dc_uid (basestring) – designCompetence uID
  • outputs_element (str) – output element to be added
Returns:

XML with output element
Return type:

XMLSchema
add_dc_performance_info(dc_uid, precision=None, fidelity_level=None, run_time=None, verification=None)

Method to add a performance info element to a DC branch.

Parameters:
  • dc_uid (basestring) – designCompetence uID
  • precision (float) – (optional) designCompetence precision
  • fidelity_level (int) – (optional) designCompetence fidelity level
  • run_time (float) – (optional) designCompetence run time
  • verification (dict) – designCompetence verification method
Returns:

XML with performance info
Return type:

XMLSchema

Note

the verification dict should contain the following keys:

  • ‘method’ (str)
  • ‘verifier’ (uid)
  • ‘result’ (str)
  • ‘date’ (datetime)
  • ‘version’ (str)
add_dc_remote_component_info(dc_uid, single_or_multi_execution, job_name, remote_engineer, notification_message, data_exchange_dict=None)

Method to add a remote execution info element to a dc branch.

add_dc_verification(dc_uid, method, verifier, result, date, version)

Method to add a verification to a DC branch.

Parameters:
  • dc_uid (basestring) – designCompetence uID
  • method (str) – verification method
  • verifier (basestring) – verifier uID
  • result (str) – verification result
  • date (datetime) – verification date
  • version – designCompetence uID that was verified
Returns:

XML with verification added to DC
Return type:

XMLSchema
append_element_to_uid_element(uid, element_to_add, expected_tag_uid_el=None, expected_tag_new_el=None)

Generic method to add a subelement to an element with a certain UID.

Parameters:
  • uid (basestring) – uID of element to be added to
  • element_to_add (str) – subelement to be added
  • expected_tag_uid_el (str) – expected tag of element
  • expected_tag_new_el (str) – expected tag of subelement
Returns:

XML with subelement
Return type:

XMLSchema
add_header(creator, description, timestamp=None, fileVersion='0.0', cmdowsVersion='0.7')

Method to add a header to a CMDOWS file.

Parameters:
  • creator (str) – name of creator
  • description (str) – description of file content
  • timestamp (datetime) – (optional) date and time of creation
  • fileVersion (str) – version of the file
  • cmdowsVersion (str) – version of the xsd schema
Returns:

XML with header
Return type:

XMLSchema
add_new_parameters_from_element(parameters_element)

Method to add the new parameters based on a parameters element.

Parameters:parameters_element (element) – new parameters
Returns:enriched parameters branch
Return type:XMLSchema
add_subelement(element, subelement_xpath_tag)

Method to add a subelement to an existing element.

Parameters:
  • element (element) – element to be added to
  • subelement_xpath_tag (xpath/tag) – subelement to be added.
Returns:

enriched element branch
Return type:

XMLSchema
assert_element_tag(el, expected_tag)

Method to assert that the tag of an element is as expected.

Parameters:
  • el – element
  • expected_tag (str) – expected tag
Rtype el:

str
check()

Method to execute all checks and remove unused contacts

Returns:overall check result
Return type:bool
check_references()

Method to check if references are actually pointing to a uID in a CMDOWS file

Returns:result of reference check
Return type:bool
check_schema()

Method to check if a CMDOWS file adheres to its schema

Returns:result of schema check
Return type:bool
check_uids()

Method to check if all uIDs are actually unique in a CMDOWS file

Returns:result of unique uID check
Return type:bool
ensure_abs_xpath(xpath)

Method to ensure that the elements given by an absolute XPath exist.

Parameters:xpath (basestring) – XPath to be checked
Returns:checked XPath
Return type:basestring
get_design_competences_uids()

Method to get a list of all the design competences UIDs present in the file.

Returns:design competence uIDs
Return type:list
get_element_of_uid(uid, expected_tag=None)

Method to get the element based on a UID value.

Parameters:
  • uid (basestring) – uID of element
  • expected_tag (str) – (optional) expected tag
Returns:

element
Return type:

str
get_executable_blocks_uids()

Method to get a list of all the executable block UIDs present in the file.

Returns:executable blocks uIDs
Return type:list
get_inputs_uids(exblock_uid)

Method to collect the inputs of a CMDOWS file executableBlock entry

Parameters:exblock_uid (basestring) – uid of the executableBlock entry
Returns:path to input
Return type:xpath
get_mathematical_functions_uids()

Method to get a list of all the mathematical functions UIDs present in the file.

Returns:mathematical functions uIDs
Return type:list
get_outputs_uids(exblock_uid)

Method to collect the outputs of a CMDOWS file executableBlock entry

Parameters:exblock_uid (basestring) – executableBlock entry
Returns:path to output
Return type:xpath
get_parameters_uids()

Method to get a list of all the parameter UIDs present in the file.

Returns:parameter uIDs
Return type:list
get_used_parameter_uids()

Method to get a list of all the parameter UIDs used in the file.

Returns:parameter uIDs
Return type:list
get_xpath_of_uid(uid, expected_tag=None)

Method to get the xpath based on a UID value.

Parameters:
  • uid (basestring) – uID of xpath
  • expected_tag (str) – (optional) expected_tag
Returns:

xpath of element
Return type:

xpath
remove_actor(role, uid)

Method to remove a role from the organization for a certain contact.

Parameters:
  • role (str) – role to be removed from contact
  • uid (basestring) – uID of contact
Returns:

Organization branch without contact role
Return type:

XMLSchema

Note

Role options are:

  • ‘architect’
  • ‘integrator’
  • ‘collaborativeEngineer’
  • ‘toolSpecialist’
  • ‘customer’
remove_children_of_uid(uid, children_to_remove='__all__', children_to_keep='__none__')

Method to remove the children of a CMDOWS file element based on a uID.

Parameters:
  • uid (basestring) – uID of the element to remove the children of
  • children_to_remove (element) – (optional) list of children to be removed (default: remove all)
  • children_to_keep (element) – (optional) list of children to be kept (default: keep none)
Returns:

element with (certain) children removed
Return type:

XMLSchema

Attention

make sure the child elements contained in the two lists do not overlap.

remove_children_of_xpath(xpath, children_to_remove='__all__', children_to_keep='__none__')

Method to remove the children of a CMDOWS file element based on an XPath.

Parameters:
  • xpath (basestring) – XPath of the element to remove the children of
  • children_to_remove (element) – (optional) list of children to be removed (default: remove all)
  • children_to_keep (element) – (optional) list of children to be kept (default: keep none)
Returns:

element with (certain) children removed
Return type:

XMLSchema

Attention

make sure the child elements contained in the two lists do not overlap.

remove_contact(contact_uid)

Method to remove a contact based on its uID.

Parameters:contact_uid (basestring) – uID of the contact to be removed
Returns:Schema without contact
Return type:XMLSchema
remove_data_graph_element()

Method to remove a dataGraph element from a CMDOWS file.

Returns:Schema without dataGraph
Return type:XMLSchema
remove_element_based_on_uid(uid, expected_tag=None)

Method to remove an element based on its uID.

Parameters:
  • uid (basestring) – uID of element to be removed
  • expected_tag (str) – (optional) the expected tag of the element
Returns:

Schema without element
Return type:

XMLSchema
remove_element_based_on_xpath(xpath, expected_amount=None, expected_text=None, higher_level_removal=None)

Method to remove an element based on its XPath.

Parameters:
  • xpath (basestring) – XPath of element to be removed
  • expected_amount (int) – (optional) the expected amount of elements with this path
  • expected_text (str) – (optional) the expected text of the element
  • higher_level_removal (int) – (optional) the amount of higher levels to be removed
Returns:

Schema without element
Return type:

XMLSchema
remove_in_and_outputs(exblock_uid)

Method to remove the in- and outputs of a CMDOWS file executableBlock entry.

Parameters:exblock_uid (basestring) – executableBlock to remove the in- and outputs from
Returns:executableBlock without in- and outputs
Return type:XMLSchema
remove_inputs(exblock_uid)

Method to remove the inputs of a CMDOWS file executableBlock entry.

Parameters:exblock_uid (basestring) – executableBlock to remove the inputs from
Returns:executableBlock without inputs
Return type:XMLSchema
remove_outputs(exblock_uid)

Method to remove the outputs of a CMDOWS file executableBlock entry.

Parameters:exblock_uid (basestring) – executableBlock to remove the outputs from
Returns:executableBlock without outputs
Return type:XMLSchema
remove_parameter(param_uid)

Method to remove a parameter based on its uID.

Parameters:param_uid (basestring) – uID of parameter to be removed
Returns:Schema without parameter
Return type:XMLSchema
remove_parameters(params_uids)

Method to remove a list of parameters based on their uID.

Parameters:params_uid – uIDs of parameters to be removed
Returns:Schema without parameters listed
Return type:XMLSchema
remove_parameters_element()

Method to remove a parameters element from a CMDOWS file.

Returns:Schema without parameters
Return type:XMLSchema
remove_process_graph_element()

Method to remove a processGraph element from a CMDOWS file.

Returns:Schema without processGraph
Return type:XMLSchema
remove_unused_contacts()

Method to check if there are uID in CMDOWS file which are not referred to and remove them

Returns:XML without unused uID’s
Return type:XMLSchema
remove_workflow_element()

Method to remove a workflow element from a CMDOWS file.

Returns:Schema without workflow
Return type:XMLSchema
resolve_uids()

Method to rename duplicate uIDs in a CMDOWS file.

Returns:renamed duplicate uIDs
Return type:XMLSchema
save(file_path=None, pretty_print=False, method='xml', xml_declaration=True, encoding='UTF-8')

Method to save a manipulated CMDOWS file.

Parameters:
  • file_path (basestring) – file path of the CMDOWS
  • pretty_print (bool) – (optional)
  • method (str) – (optional) output method (see options below, default: “xml”)
  • xml_declaration (bool) – (optional) addition of XML declaration to file
  • encoding (str) – (optional) output encoding (see options below, default: “UTF-8”)

Note

Output method options:

  • “xml”
  • “html”
  • “text”
  • “c14n”

Note

Output encoding options:

  • “UTF-8”
  • “US-ASCII”
schema()

Method to retrieve a schema either belonging to the CMDOWS file

Returns:schema
Return type:XMLSchema
simplify()

Method to simplify everything

simplify_equations()

Method to replace duplicate equations elements by a single equations element and refs to this element

Returns:XML with simplified equation
Return type:XMLSchema
version()

Method to retrieve the version of the CMDOWS file

Returns:version
Return type:str

Utilities

Below the utilities used by KADMOS are listed.

General

kadmos.utilities.general.assert_dict_keys(dic, expected_keys, all_keys_required=False)

Method to assert that a dictionary has the expected keys and (optionally) to check if is has all the keys.

Parameters:
  • dic (dict) – dictionary to checked
  • expected_keys (list) – keys expected in dictionary
  • all_keys_required (bool) – (optional) to check if all keys are in the dictionary
Returns:

check results
kadmos.utilities.general.color_list()

A list of distinguisable colors.

Returns:list with HTML Hex colors
kadmos.utilities.general.convert_bytes(num)

Function to convert bytes to KB, MB, GB, TB.

Parameters:num (int) – number of bytes
Returns:converted number
Return type:str
kadmos.utilities.general.dict_to_ord_dict(dic, key_order)

Method to transform a Python dictionary into a Python ordered dictionary.

Parameters:
  • dic (dict) – dictionary
  • key_order (list) – order of the keys
Returns:

ordered dictionary
Return type:

OrderedDict

Note

the key_order list can have items that are not present in the given dictionary. All keys of the dictionary should be in the order though.

kadmos.utilities.general.export_as_json(data, filename, indent=None, sort_keys=True, cwd=None)

Function to export a data object to a json file.

Parameters:
  • data (dict or list) – object with the data to be exported
  • filename (basestring) – name of the json file
  • indent (int) – number of spaces for one indentation
  • sort_keys (bool) – option for sorting keys
  • cwd (None, str) – current working directory
Returns:

json file
Return type:

file
kadmos.utilities.general.file_size(file_path)

Function to return the file size.

Parameters:file_path (str) – file path
Returns:file size
Return type:str
kadmos.utilities.general.file_size_MB(file_path)

Function to return the file size in MB.

Parameters:file_path (str) – file path
Returns:file size in MB
Return type:float
kadmos.utilities.general.filter_group_vars(group_dict, current_group_idx, filter_setting)

Method to get the variables of a local/external group.

Parameters:
  • group_dict (dict) – local/external group
  • current_group_idx (int) – index of the current group
  • filter_setting (list, string) – filter on ‘local’ or ‘external’ groups
Returns:

group variables
Return type:

list
kadmos.utilities.general.format_string_for_latex(string, prefix='', suffix='')

Function to format a string such that it can be used in LaTeX.

Parameters:
  • string (str) – string to be formatted
  • prefix (basestring) – prefix to be placed in front of the string
  • suffix (basestring) – suffix to be appended to the string
Returns:

formatted string
Return type:

basestring
kadmos.utilities.general.format_string_for_vistoms(string, prefix='', suffix='')

Function to format a string such that it can be used in VISTOMS.

Parameters:
  • string (str) – string to be formatted
  • prefix (basestring) – prefix to be placed in front of the string
  • suffix (basestring) – suffix to be appended to the string
Returns:

formatted string
Return type:

basestring
kadmos.utilities.general.format_string_for_vistoms_new(string, prefix='', suffix='')

Function to format a string such that it can be used in VISTOMS.

Parameters:
  • string (str) – string to be formatted
  • prefix (basestring) – prefix to be placed in front of the string
  • suffix (basestring) – suffix to be appended to the string
Returns:

formatted string
Return type:

basestring
kadmos.utilities.general.get_element_dict(xpath, var_value=None, var_dim=None, include_reference_data=False)

Function to create a D3.js-type dictionary for a nested tree based on an xpath.

Parameters:
  • xpath (basestring) – xpath for the element
  • var_value (float) – value of the element in a reference file
  • var_dim (int) – dimension of the element in a reference file
  • include_reference_data (bool) – setting on whether reference data should be include in the path
Returns:

nested dictionary
Return type:

dict
kadmos.utilities.general.get_friendly_id(uid_length)

Create a recognisable ID string.

Parameters:uid_length (int) – length of the ID
Returns:new ID
Return type:str
kadmos.utilities.general.get_group_vars(sa, current_group_idx)

Method to get the variables with respect to the subgroup.

Parameters:
  • sa (dict) – system analysis dictionary
  • current_group_idx (int) – index of the subgroup
Returns:

multiple lists with variables
Return type:

tuple
kadmos.utilities.general.get_list_entries(*args)

Utility to return only certain values of a given list based on the indices.

Parameters:args (list and int) – list and indices
Returns:list with requested values at indices
Return type:list
kadmos.utilities.general.get_mdao_setup(mdao_setup)

Simple function to specify the MDAO architecture and convergence type based on a single string.

Parameters:mdao_setup (str) – MDF-GS, MDF-J, IDF
Returns:mdo_architecture, mda_type, allow_unconverged_couplings
Return type:str
kadmos.utilities.general.get_uid(preference, uids)

Simple function to determine a valid uid which is not part of the uids list

Parameters:
  • preference (str) – preferred name for the uid
  • uids (list) – list of existing uids
Returns:

a valid uid which
Return type:

str
kadmos.utilities.general.get_unique_friendly_id(used_ids, uid_length)

Return an ID that is not in our list of already used IDs.

Parameters:
  • used_ids (list) – already used IDs
  • uid_length (int) – length of the ID
Returns:

new unique ID
Return type:

str
kadmos.utilities.general.hex_to_rgb(value)

Function to translate a hex color string to an RGB color tuple.

Parameters:value (str) – HTML hex color
Returns:RGB colors
Return type:tuple
kadmos.utilities.general.make_camel_case(string, make_plural_option=False)

Function to make a string camelCase.

Parameters:
  • string (str) – non-camelcase string
  • make_plural_option (bool) – pluralize camelcase string
Returns:

camelcase string
Return type:

str
kadmos.utilities.general.make_plural(string)

Function to convert a string to its plural form.

Parameters:string (str) – initial string
Returns:plural string
Return type:str
kadmos.utilities.general.make_singular(string)

Function to convert a string to its singular form.

Parameters:string (str) – initial string
Returns:singular string
Return type:str
kadmos.utilities.general.open_file(filename)

Utility to open a file cross-platform.

Parameters:filename – Filename including extension and path, e.g. ‘sampledir/samplefile.pdf’
Returns:An opened file
kadmos.utilities.general.remove_if_exists(input_list, entries_to_remove)

Utility to remove certain values from a list.

Parameters:
  • input_list (list) – initial list
  • entries_to_remove (list) – values to remove
Returns:

list with removed entries
Return type:

list
kadmos.utilities.general.test_attr_cond(attr_value, operator, test_value)

Function to check a given conditional statement and return True or False.

Parameters:
  • attr_value (str, float, int) – value of the actual attribute
  • operator (str) – conditional operator to be used (‘<’,’<=’,’==’,’!=’,’>=’,’>’, ‘in’)
  • test_value (str, float, int) – value to which the attribute value should be compared.
Returns:

result of the conditional statement.
Return type:

bool
kadmos.utilities.general.transform_data_into_strings(data, keys_to_be_removed=[])

Utility function to transform certain data types in a dictionary into strings.

Parameters:
  • data (dict) – dictionary with data
  • keys_to_be_removed (list) – list of keys that have to be removed from the dict
Returns:

adjusted dictionary
Return type:

dict
kadmos.utilities.general.transform_string_into_format(data, keys_to_be_removed=[])

Utility function to transform certain strings back into their original data format (NoneType, list, etc.).

Parameters:
  • data (dict) – dictionary with data
  • keys_to_be_removed (list) – list of keys that have to be removed from the dict
Returns:

adjusted dictionary
Return type:

dict
kadmos.utilities.general.translate_dict_keys(dictionary, translations)

Utility to (recursively) translate all keys in a dictionary with a translation dictionary.

Parameters:
  • dictionary (dict) – dictionary to be translated
  • translations (dict) – dictionary used for the translation
Returns:

translated dictionary
Return type:

dict
kadmos.utilities.general.translate_list(l, dictionary)

Utility to quickly translate all elements in a list with a dictionary.

Parameters:
  • l (list) – list to be translated
  • dictionary (dict) – dictionary used for the translation
Returns:

translated list
Return type:

list
kadmos.utilities.general.unmake_camel_case(string, separator='_')

Function to make camelCase a string with separator (e.g. underscores).

Parameters:
  • string (str) – camelCase string
  • separator (str) – symbol/symbols used as separator
Returns:

string with separator
Return type:

str

XML

class kadmos.utilities.xml.ExtendedElement
add(tag, value=None, attrib=None, camel_case_conversion=False, only_add_if_valued=False, **extra)

Method to add a new sub element to the element

Parameters:
  • tag (str) – The sub element tag
  • value (None, str, list, dict, OrderedDict) – The sub element value
  • camel_case_conversion (bool) – Option for camel case convention
  • attrib – An optional dictionary containing sub element attributes
  • extra – Additional sub element attributes given as keyword arguments
Returns:

An element instance
Return type:

Element
add_process_hierarchy(process_hierarchy, mpg)

Method to add the hierarchy of the process as metadata to the workflow/processGraph.

clean()

Method to recursively remove empty subelements from the Element

findasttext(path=None, namespaces=None)

Method which extends the findtext method by trying to evaluate the string with the ast module

finddict(path_or_element, namespaces=None, ordered=True, camel_case_conversion=False)

Method which reverses the add method and creates a dictionary from an element

Parameters:
  • path_or_element (str, Element) – (Path to) the element to be analysed
  • namespaces – A prefix-to-namespace mapping that allows the usage of XPath prefixes in path_or_element
  • ordered (bool) – Option for creation of a OrderedDict or a dict respectively
  • camel_case_conversion (bool) – Option for conversion of the element tags from camelCase to lower_case
Returns:

A dictionary
Return type:

OrderedDict, dict
kadmos.utilities.xml.get_element_details(tree, xpath)

Function to determine the value and dimension of an UXPath element in a reference file.

Parameters:
  • tree – ElementTree object used for finding the XPath
  • xpath – XPath
Returns:

element value and dimension
kadmos.utilities.xml.get_uid_search_xpath(uid)

Method to get the XPath expression for a UID which might contain quote characters.

Parameters:uid (str) – uID string
Returns:XPath expression
Return type:str
kadmos.utilities.xml.merge(a, b)

Recursive function to merge a nested tree dictionary (D3.js convention) into a full tree dictionary.

Parameters:
  • a (dict) – full dictionary in which a new element is merged
  • b (dict) – element dictionary of the new element
Returns:

merged dictionary
Return type:

dict
kadmos.utilities.xml.recursively_empty(element)

Utility function to check recursively if a ElementTree object is empty.

Parameters:element – Input ElementTree object
Returns:Result of the check
kadmos.utilities.xml.recursively_stringify(tree)

Utility function to recursively stringify a ElementTree object (for file comparison).

Parameters:tree – Input ElementTree object
Returns:List of strings representing the ElementTree object
kadmos.utilities.xml.recursively_unique_attribute(element, attribute='uID')

Utility function to check recursively if the values of an attribute of an ElementTree object are unique.

Parameters:
  • element – Input ElementTree object
  • attribute – Name of the attribute being checked for uniqueness
Returns:

Result of the check

Strings

kadmos.utilities.strings.find_between(s, first, last)

Function to find a target string that is located between two particular strings.

Parameters:
  • s (basestring) – string to search
  • first (basestring) – the string in front of the target string
  • last (basestring) – the string behind the target string
Returns:

the target string
Return type:

basestring
kadmos.utilities.strings.find_until(s, until)

Function to find a target string that is located before a particular string.

Parameters:
  • s (basestring) – string to search
  • until (basestring) – the string signifying the end of the target string
Returns:

target string
Return type:

basestring
kadmos.utilities.strings.get_correctly_extended_latex_label(label, label_extension)

Function to obtain a correctly extended latex label.

Parameters:
  • label (str) – label to be extended
  • label_extension (str) – extensions of the label
Returns:

extended latex label
Return type:

str

Printing

kadmos.utilities.printing.print_in_table(print_container, **kwargs)

This function prints a table using the provided data and the headers. The “tabulate” library is used.

Parameters:
  • print_container (iterable) – container for iterables –> [[row1], [row2], …]
  • kwargs (str, bool) – keyword arguments for printing

Note

Kwargs options:

  • ‘header’ (str)
  • ‘message’ (str)
  • ‘print_indeces’ (bool)
kadmos.utilities.printing.print_indexed_list(*args, **kwargs)

This function prints an indexed column of the given arguments in the console. The provided message is printed above the indexed column. Can be used in combination with user_prompt_select_options().

Parameters:
  • kwargs – message: Message printed above indexed column
  • kwargs – index_bracket: Type of bracket used for indeces in list (“round” or “squared”), default is round
  • args – Arguments listed in column by index
Returns:

printed indexed column of arguments in console

Example use:

>>> print_indexed_list(message="These are the tools: ", index_bracket="round", *list_of_tools)

KnowledgeBase

!! N.B. This class will be deprecated in future versions. !!

Below the full KADMOS KnowledgeBase class description can be found.

class kadmos.knowledgebase.knowledgebase.KnowledgeBase(kb_dir_path, knowledge_base, ignore_functions=None)

Class that imports the data stored in a knowledge base. This is a start point for graph visualizations.

get_function_dependencies(funcName, mode, inOut=None)

This function builds a directed graph (KadmosGraph object) for the specified function using the “networkx” package. If inOut argument is specified, only the input or output of the function will be included in the graph, otherwise both.

Parameters:
  • funcName (str) – function name for which the graph is generated; must be present in knowledge base.
  • inOut (str) – (optional) if specified, must be “input” or “output” string. Specification of this argument enables the generation of the function graph with only input or output variables.
Returns:

functionGraph
get_function_graph(funcName, inOut=None)

This function builds a directed graph (KadmosGraph object) for the specified function using the “networkx” package. If inOut argument is specified, only the input or output of the function will be included in the graph, otherwise both.

Parameters:
  • funcName (str) – function name for which the graph is generated; must be present in knowledge base.
  • inOut (str) – (optiona) if specified, must be “input” or “output” string. Specification of this argument enables the generation of the function graph with only input or output variables.
Returns:

functionGraph
get_kb_graphs()

This class method generates all graphs for all present functions in the knowledge base.

Returns:self.functionGraphs
get_rcg(name='RCG')

Function to create Maximal Connectivity Graph (Pate, 2014) by composing a list of graphs.

Returns:maximal connectivity graph (RCG)
print_circular_connections_in_log()

This function prints the circular connections present in the graph.

Param:self.circularConnections: Dict with circular connections per tool
Returns:print to console