Welcome to KADMOS’s documentation!

Contents:

Indices and tables

Documentation for the Code

The KADMOS package is fully described in this documentation.

MDOproblem

Below the full KADMOS MDOproblem class description can be found.

class pyKADMOS.MDOproblem.MDOproblem(knowledge_base)

Class that can be used to formally specify an MDO problem and analyze it based on graph theoretical analyses.

analyze_function_files(print_in_log=True)

Function that analyzes all tool files (input, output, info) and returns all the xpaths in the xml file plus a dictionary with the nodes that have a value.

Parameters:print_in_log – Boolean on whether the results should be printed to the log
Returns:function_data dict with all function data like ‘input’, ‘output’, ‘unique_xpaths’, ‘sorted_xpaths’
analyze_function_xml(xml_file)

Function that analyzes a single tool XML input or output file.

Parameters:xml_file – name of XML file to be analyzed
Returns:dict with collection of XML file properties, valued_nodes, and all_xpaths
check_against_xsd()

Check the XML files in the knowledge base against the XML Schema.

Return type:Error
get_FPG_based_on_sink(sink)

Function to get the Fundamental Problem Graph based on the required output variable.

Parameters:sink – node name of desired variable
Returns:Fundamental problem graph (FPG) object
get_FPG_based_on_sinks(list_of_sinks)

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

Parameters:list_of_sinks – list with strings that specify the desired output
Returns:Fundamental Problem Graph (FPG) object
get_MCG()

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

Returns:maximal connectivity graph (MCG)
get_PSG(FPG, MDA_type, analysis_order)

Create the PSG graph for the MDF method.

Parameters:
  • FPG (DiGraph) – fundamental problem graph with required node properties
  • MDA_type (str) – type of multidisciplinary analysis to be implemented (‘Gauss-Seidel’ or ‘Jacobi’)
  • analysis_order (list) – list with the order of the analyses in the MDA
Returns:

dictionary with the PSG process flow and the PSG data flow
Return type:

dict
get_common_nodes()

Function to find the common nodes between tool inputs and outputs.

Returns:dictionary with common nodes
get_contracted_graph(graph, contraction_level)

Function to contract the nodes of a graph to a given xpath level.

Parameters:
  • graph – input graph
  • contraction_level – int from 0 (highest level) to X (lowest level existing in XML schema)
Returns:

graph with contracted nodes
get_extended_MCG()

Function to create extended maximal connectivity graph (additional variable nodes for shared variables).

Returns:extended maximal connectivity graph (MCG)
get_function_graph(function_name)

Function to automatically create the digraph of the function element.

Parameters:function_name – name of the function according to XML file names.
Returns:graph of the function.
get_function_graphs()

Function to collect all function graphs in the MDO problem class.

Returns:list with all function graph objects.
get_function_names()

Get the function names in the function_i_o_xmls based on the input XML files.

Returns:function_names list with file name strings
get_partitioned_graph(G, n_parts, tpwgts=None, recursive=False, contig=False, output='DiGraph')

Partition a graph using the Metis algorithm (http://glaros.dtc.umn.edu/gkhome/metis/metis/overview). Note that partitioning can only be performed on undirected graphs. Therefore every graph input is translated into an undirected graph.

Parameters:
  • G – graph object
  • n_parts – number of partitions requested (algorithm might provide less)
  • tpwgts – list of target partition weights
  • recursive – Metis option
  • contig – Metis option
  • output – set whether expected output is a DiGraph or normal Graph
Returns:

list of edges that have been cut
Returns:

list of partition group to which each node belongs

MDOstatic

Below the static graph functions used by KADMOS are listed.

Collection of static functions

pyKADMOS.MDOstatic.add_nodes_subcategory(graph)

Function to analyze and sort the graph nodes according to different variable descriptions.

CATEGORY OF NODE SUBCATEGORY INDEGREE OUTDEGREE
variable hole 0 0
supplied input 0 1
supplied shared input 0 >1
output 1 0
collision >1 0
coupling 1 1
shared coupling 1 >1
collided coupling >1 1
collided shared coupling >1 >1
variable group hole group 0 0
supplied input group 0 1
supplied shared input group 0 >1
output groupe >0 0
coupling groupe >0 1
shared coupling groupe >0 >1
function hole 0 0
inputless 0 >0
outputless >0 0
complete >0 >0
Parameters:graph – directed graph
Returns:dictionary with sorted variables
pyKADMOS.MDOstatic.find_all_nodes(graph, category='all', subcategory='all', attr_cond=None, print_in_log=False, print_attributes='all')

Advanced search function to get nodes and their properties.

Parameters:
  • graph (DiGraph) – input graph
  • category (list) – category of the node (you can specify multiple in a list), see note for allowed values.
  • subcategory (list) – 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])
  • 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
  • variables
  • variable groups
  • functions

Note

The following subcategories are allowed:

  • all
  • all inputs
  • all outputs
  • all couplings
  • all problematic nodes
  • holes
  • supplied inputs
  • supplied shared inputs
  • outputs
  • collisions
  • couplings
  • shared couplings
  • collided couplings
  • collided shared couplings
  • hole groups
  • supplied input groups
  • supplied shared input groups
  • output groups
  • coupling groups
  • shared coupling groups
  • holes
  • inputlesss
  • outputlesss
  • completes

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

>>> all_nodes = find_all_nodes(graph)

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

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

Get all input nodes with a certain attribute value:

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

Function to determine the adjacency matrix of a graph.

Parameters:
  • graph – input graph
  • print_in_log – Boolean on whether the results should be printed to the log
Returns:

different methods of storing the same adjacency matrix in one dictionary
pyKADMOS.MDOstatic.get_categorized_nodes(graph, print_in_log=True)

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

Parameters:
  • graph – input graph
  • print_in_log – Boolean on whether the results should be printed to the log
Returns:

dictionary with analysis results
pyKADMOS.MDOstatic.get_graph_nodes_indegree(graph)

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

Parameters:graph – DiGraph object
Returns:dictionary with node name key and indegree integer value.
pyKADMOS.MDOstatic.get_graph_nodes_outdegree(graph)

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

Parameters:graph – DiGraph object
Returns:dictionary with node name key and outdegree integer value.
pyKADMOS.MDOstatic.get_node_attributes(graph, node, attr_list, print_in_log=False)

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

Parameters:
  • graph (DiGraph) – DiGraph of the complete graph in which the node is present
  • node (str) – node name
  • attr_list (list) – list with attributes to be pulled
  • print_in_log (bool) – parameter to set printing in log on or off
Returns:

dictionary with node attributes
Return type:

dict
pyKADMOS.MDOstatic.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 (‘<’,’<=’,’==’,’!=’,’>=’,’>’)
  • test_value (str, float, int) – value to which the attribute value should be compared.
Returns:

result of the conditional statement.
Return type:

bool

MDOvisualization

Below the visualization functions used by KADMOS are listed.

pyKADMOS.MDOvisualization.color_list()

A list of distinguisable colors.

Returns:list with HTML Hex colors
pyKADMOS.MDOvisualization.draw_adjacency_matrix(G, fig_num=1, fig_size=(7, 7), show_now=True)

Draw the adjacency matrix in a plot window.

Parameters:
  • G – networkx graph
  • 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.
Returns:

window with plot
pyKADMOS.MDOvisualization.plot_graph(G, fig_num=1, fig_size=(18, 11), color_setting='default', save_as=None, show_now=True)

Function to plot a graph. Note that you need to add matplotlib.pyplot.show() at the end of your code to see the plot window.

Parameters:
  • G –
  • fig_num – figure number
  • fig_size – size of figure window
  • color_setting – choose from ‘default’, ‘sinks’, ‘categories’, ‘partitions’
  • save_as – save plot as figure file
  • show_now – Boolean whether to plot directly (pausing the execution until the plot is closed), or not.
Returns:

window with plot

MDOexport

Below the export functions used by KADMOS are listed.

Class that can be used export MDO processes defined in the MDOproblem class.

pyKADMOS.MDOexport.save_as_graphml(graph, filename)

Save the graph in the graphml extension

Parameters:
  • graph – graph object
  • filename – file name without extension
Returns:

graphml file stored in same folder