InfrastructureModels Library

root of the infrastructure model formulation type hierarchy

Constructor for an InfrastructureModels modeling object, where the infrastructure type it must be specified a priori.

Constructor for an InfrastructureModels modeling object, where data is assumed to in a multi-infrastructure network data format.

Makes a string bold in the terminal

tests if two numbers are equal, up to floating point precision

breaks up matlab strings of the form 'name = value;'

Makes a string grey in the terminal, does not seem to work well on Windows terminals more info can be found at https://en.wikipedia.org/wiki/ANSIescapecode

Initialize an empty dictionary with a structure similar to ref.

Attempts to determine if the given data is a component dictionary

Populate the portion of refs corresponding to global keys.

Populate the portion of refs for a specific infrastructure type.

Attempts to determine the type of an array of strings extracted from a matlab file

Attempts to determine the type of a string extracted from a matlab file

recursive call of updatedata

recursive call of updatedata

Apply the function func!, which modifies data for a specific infrastructure, it. Here, apply_to_subnetworks specifies whether or not func! should be applied to all subnetworks in a multinetwork dataset.

Apply the function func!, which modifies ref using data for a specific infrastructure, it. Here, apply_to_subnetworks specifies whether or not func! should be applied to all subnetworks in a multinetwork dataset.

Apply the function func!, which modifies ref for a specific infrastructure, it. Here, apply_to_subnetworks specifies whether or not func! should be applied to all subnetworks in a multinetwork dataset.

turns top level arrays into dicts

Builds a ref object without the need to initialize an AbstractInfrastructureModel, where it specifies the infrastructure type.

Builds a ref object without the need to initialize an AbstractInfrastructureModel, where the data is assumed to be in a multi-infrastructure format.

Checks if the given value is of a given type, if not tries to make it that type

tests if two dicts are equal, up to floating point precision

builds a table of component data

Given a continuous variable x and a binary variable z, sets x to 0.0 when z is 0. Requires that 0.0 is in the domain of x.

converts a float value into a string of fixed precision

sprintf would do the job but this work around is needed because sprintf cannot take format strings during runtime

Apply the getter function func, which operates on data for a specific infrastructure, it. Here, apply_to_subnetworks specifies whether or not func should be applied to all subnetworks in a multinetwork dataset. If so, a dictionary of retrieved data using func is returned, indexed by the indices of the multinetwork. Otherwise, a single value is returned.

gets the number of networks that could exist provided the base data

checks if a given dataset has a time series component

checks if a given network data is a multinetwork

checks if a given network data is a multinetwork

loads a single time point from a time_series data block into the current network

logger_config!(level::String)

Set the logging level within PowerModels. level must be one of "error", "warn", "info", or "debug".

turns a single network and a time_series data block into a multi-network

prints the text summary for a data dictionary to stdout

Given a data dictionary following the InfrastructureModels multi-infrastructure conventions, build and return an initial "ref" dictionary, converting strings to symbols and component keys to integers. The global keys argument specifies which keys should remain in the root of the dictionary when building the multi-infrastructure dataset.

Given a data dictionary following the Infrastructure Models conventions, builds an initial "ref" dictionary converting strings to symbols and component keys to integers. The global keys argument specifies which keys should remain in the root of dictionary when building a multi-network

constraint: c^2 <= a*b

constraint: c^2 + d^2 <= a*b

a conic encoding of constraint: c^2 <= a*b

a conic encoding of constraint: c^2 + d^2 <= a*b

on/off variant of relaxationcomplexproduct controlled by indicator variable z in the conic form

on/off variant of relaxationcomplexproduct controlled by indicator variable z in the conic form

on/off variant of relaxationcomplexproduct controlled by indicator variable z, variant with a fixed value of z

on/off variant of relaxationcomplexproduct controlled by indicator variable z

on/off variant of relaxationcomplexproduct controlled by indicator variable z

an on/off variant of x == y, controlled by the indicator variable z

an on/off variant of x == y, controlled by the indicator variable z, a variant for fixed z

general relaxation of binlinear term (McCormick)

z >= JuMP.lower_bound(x)*y + JuMP.lower_bound(y)*x - JuMP.lower_bound(x)*JuMP.lower_bound(y)
z >= JuMP.upper_bound(x)*y + JuMP.upper_bound(y)*x - JuMP.upper_bound(x)*JuMP.upper_bound(y)
z <= JuMP.lower_bound(x)*y + JuMP.upper_bound(y)*x - JuMP.lower_bound(x)*JuMP.upper_bound(y)
z <= JuMP.upper_bound(x)*y + JuMP.lower_bound(y)*x - JuMP.upper_bound(x)*JuMP.lower_bound(y)

On/Off variant of a relaxed binlinear term (McCormick) requires that all variables (x,y,z) go to zero with ind Variant where ind is a fixed value and not a variable

On/Off variant of a relaxed binlinear term (McCormick) requires that all variables (x,y,z) go to zero with ind

general relaxation of a square term

x^2 <= y <= (JuMP.upper_bound(x)+JuMP.lower_bound(x))*x - JuMP.upper_bound(x)*JuMP.lower_bound(x)

convex hull relaxation of trilinear term

w₁ = JuMP.lower_bound(x)*JuMP.lower_bound(y)*JuMP.lower_bound(z)
w₂ = JuMP.lower_bound(x)*JuMP.lower_bound(y)*JuMP.upper_bound(z)
w₃ = JuMP.lower_bound(x)*JuMP.upper_bound(y)*JuMP.lower_bound(z)
w₄ = JuMP.lower_bound(x)*JuMP.upper_bound(y)*JuMP.upper_bound(z)
w₅ = JuMP.upper_bound(x)*JuMP.lower_bound(y)*JuMP.lower_bound(z)
w₆ = JuMP.upper_bound(x)*JuMP.lower_bound(y)*JuMP.upper_bound(z)
w₇ = JuMP.upper_bound(x)*JuMP.upper_bound(y)*JuMP.lower_bound(z)
w₈ = JuMP.upper_bound(x)*JuMP.upper_bound(y)*JuMP.upper_bound(z)
w = λ₁*w₁ + λ₂*w₂ + λ₃*w₃ + λ₄*w₄ + λ₅*w₅ + λ₆*w₆ + λ₇*w₇ + λ₈*w₈
x = (λ₁ + λ₂ + λ₃ + λ₄)*JuMP.lower_bound(x) + (λ₅ + λ₆ + λ₇ + λ₈)*JuMP.upper_bound(x)
y = (λ₁ + λ₂ + λ₅ + λ₆)*JuMP.lower_bound(y) + (λ₃ + λ₄ + λ₇ + λ₈)*JuMP.upper_bound(y)
z = (λ₁ + λ₃ + λ₅ + λ₇)*JuMP.lower_bound(z) + (λ₂ + λ₄ + λ₆ + λ₈)*JuMP.upper_bound(z)
λ₁ + λ₂ + λ₃ + λ₄ + λ₅ + λ₆ + λ₇ + λ₈ = 1

Transforms a single network into a multinetwork with several deepcopies of the original network

takes a row from a matrix and assigns the values names

takes a row from a matrix and assigns the values names and types

silence()

Silence logging within InfrastructureModels.

This is equivalent to calling logger_config!("error").

given a constant value, builds the standard component-wise solution structure

given a variable that is indexed by component ids, builds the standard solution structure

maps asymmetric edge variables into components

prints the text summary for a data dictionary to IO

recursively applies new_data to data, overwriting information

converts a float to a string, using float_precision cutoff

converts any value to a string

converts any value to a string, summarizes arrays

converts any value to a string, summarizes dicts

Computes the valid domain of a given JuMP variable taking into account bounds and the varaible's implicit bounds (e.g. binary).

The def macro is used to build other macros that can insert the same block of julia code into different parts of a program. In InfrastructureModels packages this is macro is used to generate a standard set of fields inside a model type hierarchy.

a macro for adding the standard InfrastructureModels fields to a type definition