mat_dp_core.measure¶

Measure is the workhorse of Mat-dp core; resources, constraints, processes, and an objective function have set a stage and provided inputs, and Measure will solve the system according to these.

Objective Example Continued¶

The most basic usage of measure should not be too difficult, see the complete code for the '3 power plants' example below:

from mat_dp_core import Resources, Processes, GeConstraint, LeConstraint, Measure

r = Resources()
electricity = r.create("electricity", "kwh")
carbon_dioxide = r.create("CO2", "tonnes")

p = Processes()
coal_plant = p.create("Coal Power Plant", (carbon_dioxide, 1), (electricity, 100))
gas_plant = p.create("Gas Power Plant", (carbon_dioxide, 0.6), (electricity, 100))
organic_plant = p.create("Organic Mass Power Plant", (carbon_dioxide, 0.8), (electricity, 100))
environment = p.create("The Atmosphere", (carbon_dioxide, -1))
grid = p.create("The Energy Grid", (electricity, -100))

coal_capacity = LeConstraint("Coal Plant Capacity 2,000kwh", coal_plant, 20)
gas_capacity = LeConstraint("Gas Plant Capacity 1,500kwh", gas_plant, 15)
organic_capacity = LeConstraint("Organic Plant Capacity 1,000kwh", organic_plant, 10)
grid_needs = GeConstraint("Grid Requirement of 2,200kwh", grid, 22)

objective = environment

# constraints must be passed to measure in a list, so we put them in a list here.
constraints = [coal_capacity, gas_capacity, organic_capacity, grid_needs]

# To solve this system, only 4 parameters need to be passed:
measure = Measure(resources=r, processes=p, constraints=constraints, objective=objective)


# For display purposes:
print("\n\n\nTonnes of CO2 produced by process:\n")
for process in measure.resource(resource=carbon_dioxide):
    print(str(process[0].name).ljust(30) + ":  " + str(round(process[1], 1)))

print("\n\n")

print("kwh of electricity produced by process:\n")
for process in measure.resource(resource=electricity):
    print(str(process[0].name).ljust(30) + ":  " + str(round(process[1], 1)))

"""


>>> Tonnes of CO2 produced by process:

>>> Coal Power Plant              :  0.0
>>> Gas Power Plant               :  9.0
>>> Organic Mass Power Plant      :  5.6
>>> The Atmosphere                :  -14.6
>>> The Energy Grid               :  0.0



>>> kwh of electricity produced by process:

>>> Coal Power Plant              :  0.0
>>> Gas Power Plant               :  1500.0
>>> Organic Mass Power Plant      :  700.0
>>> The Atmosphere                :  0.0
>>> The Energy Grid               :  -2200.0
"""

Measure()¶

The Measure class is responsible for solving a system.
Just one Measure object is made to handle this.

Summary:
Creates an instance of the Measure class, which will immediately attempt to solve the MAT-DP system.

Parameters:

resources Resources
Resources object
processes Processes
Processes object
constraints [constraints]
A list of all constraints involved in the system.
objective the objective function
The objective function being applied to this system.
maxiter int - OPTIONAL, default None
Allows assignment of the maximum number of iterations for the solver to run
allow_inconsistent_order_of_mag bool - OPTIONAL, default False
Permits inconsistent orders of magnitude (advanced).

Return Type: Measure

Location: measure.py - class Measure

Example Code:

measure = Measure(resources=r, processes=p, constraints=constraints, objective=objective)

Methods¶

`.run()`¶

Summary:
Returns the number of runs for a particular process or all the processes in the system. Optionally returns these with consideration for bounds.

Location: measure.py - class Measure

Option 1¶

Parameters:

bounds bool
Specifies whether bounds should be considered.

Return Type: [[_processes, run_vector, run_vector_lb, run_vector_ub], [...]] or [[_processes, run_vector]]

Option 2¶

Parameters:

process process variable
Specifies which specific process' run count should be returned.
bounds bool
Specifies whether bounds should be considered.

Return Type: [runs, runs_lb, runs_ub] or [runs]

`.resource()`¶

Summary:
Used to return information regarding resource input or output in relation to processes - for a full map of 'resource responsibility', see cumulative_resource(). Available with various options.

Location: measure.py - class Measure

Option 1¶

Description:
Returns measurements for all processes and resources.

Parameters:

bounds bool
Whether or not to calculate bounds.

Return Type: list

Option 2¶

Description:
Returns input and output resource values for the process specified.

Parameters:

process process variable
A process to be measured.
bounds bool
Whether or not to calculate bounds.

Return Type: list

Option 3¶

Parameters:

resource
The resource to be measured.
bounds
Whether or not to calculate bounds.

Return Type: list

Option 4¶

Parameters:

process process variable
The process to be measured.
resource resource variable
The resource to be measured.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

`.flow()`¶

Summary:
Used to return information regarding flow of resources between directly linked processes. Available with various options.

Location: measure.py - class Measure

Option 1¶

Summary:
Returns all flows between each process pair and each resource.

Parameters:

bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 2¶

Summary:
Returns all flows between the process pair specified.

Parameters:

process_from process variable
The process that the resource is flowing from.
process_to process variable
The process that the resource is flowing into.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 3¶

Summary:
Returns all flows for the resource specified.

Parameters:

resource resource variable
The resource to measure flows for.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 4¶

Summary:
Returns the sum of all outflows from this process for each resource.

Parameters:

process_from process variable
The process that the resources are flowing from.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 5¶

Summary:
Returns the sum of all outflows from this process for this resource.

Parameters:

process_from process variable
The process material is flowing from.
resource resource variable
The resource that is flowing.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 6¶

Summary:
Returns the sum of all inflows into this process for each resource.

Parameters:

process_to process variable
The process material is flowing to.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 7¶

Summary:
Returns the sum of all inflows into this process for this resource.

Parameters:

process_to process variable
The process that the resource is flowing into.
resource resource variable
The resource to measure.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 8¶

Summary:
Returns the value of resource flow for the given process pair and resource.

Parameters:

process_from process variable
The process that the resource is flowing from.
process_to process variable
The process that the resource is flowing into.
resource resource variable
The resource to measure.
bounds bool
Whether or not to calculate bounds.

Return Type: list

`.cumulative_resource()`¶

Summary:
Calculates resources leading up to all processes, or a given process. For example, all the steel contributing the manufacture of a boat. Available with various options.

Location: measure.py - class Measure

Option 1¶

Summary:
Returns the amount of each resource used for the entire chain of processes that led to each process.

Parameters:

bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 2¶

Summary:
Returns the amount of each resource used for the entire chain of processes that led to this process.

Parameters:

process process variable
The process that the resource is flowing into.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 3¶

Summary:
Returns the amount of a given resource used for the entire chain of processes that led to each process.

Parameters:

resource resource variable
The resource to be measured.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Option 4¶

Summary:
Returns the amount of a given resource used for the entire chain of processes that led to this process.

Parameters:

process process variable
The process that the resource is flowing into.
resource resource variable
The resource to be measured.
bounds bool - OPTIONAL, default False
Whether or not to calculate bounds.

Return Type: list

Example Code¶

Overview

The below worked example shows the (simplified) CO2 side effects leading to steel rail and railway wagon manufacture (assuming 500 metres and 5 wagons produced).

See the below diagram for an overview of the system:

from mat_dp_core import Resources, Processes, GeConstraint, LeConstraint, Measure

r = Resources()
iron_ore = r.create("Iron Ore", "metric tonnes")
iron = r.create("Iron", "metric tonnes")
coal = r.create("Coal", "metric tonnes")
carbon_dioxide = r.create("CO2", "metric tonnes")
steel = r.create("Steel", "metric tonnes")
rail = r.create("Steel Rail", "metres")
wagon = r.create("Railway Wagon", "units")

p = Processes()
coal_mine = p.create("Coal Mine", (carbon_dioxide, 0.2), (coal, 1.0))
iron_mine = p.create("Iron Ore Mine", (carbon_dioxide, 0.2), (iron_ore, 1.0))
iron_furnace = p.create("Iron Blast Furnace", (carbon_dioxide, 0.5), (iron, 1.0), (coal, -0.4), (iron_ore, -1.2))
steel_mill = p.create("Steel Mill", (carbon_dioxide, 0.7), (iron, -0.9), (coal, -0.5), (steel, 1.0))
rail_factory = p.create("Rail Manufacturing Factory", (carbon_dioxide, 0.1), (steel, -0.1), (rail, 1.0))
rail_wagon_factory = p.create("Wagon Manufacturing Factory", (carbon_dioxide, 0.3), (steel, -4.0), (wagon, 1.0))
environment = p.create("The Atmosphere", (carbon_dioxide, -1.0))

rail_buy = p.create("Economy demanding rail", (rail, -1))
wagon_buy = p.create("Economy demanding wagons", (wagon, -1))

# Constraints
rail_demand = GeConstraint("Demand for rail", rail_buy, 500)
wagon_demand = GeConstraint("Demand for wagons", wagon_buy, 5)

objective = environment

constraints = [rail_demand, wagon_demand]

measure = Measure(r, p, constraints, objective)

# .run()

print("---\nNumber of runs for every process:")
print(measure.run(bounds=False)) # Option 1
print("---\nNumber of runs of the coal mine only:")
print(measure.run(process=coal_mine, bounds=False)) # Option 2

# .resource()
print("---\nNet immediate resource production for every process:")
print(measure.resource(bounds=False)) # Option 1
print("---\nIron Mine is directly responsible for the following Resources:")
print(measure.resource(process=iron_mine,bounds=False)) # Option 2
print("---\nThe following Processes are directly responsible for the following amounts of Carbon Dioxide:")
print(measure.resource(resource=carbon_dioxide, bounds=False)) # Option 3
print("---\nThe Rail Factory is directly responsible for this much Carbon Dioxide:")
print(measure.resource(process=rail_factory, resource=carbon_dioxide, bounds=False)) # Option 4

# .flow()

print("---\n\n1) All flows from everything to everything:")
print(measure.flow()) # Option 1

print("---\n\n2) Flow of all resources from the iron mine to the iron furnace:")
print(measure.flow(process_from=iron_mine, process_to=iron_furnace)) # Option 2

print("---\n\n3) All flows of iron:")
print(measure.flow(resource=iron)) # Option 3

print("---\n\n4) The sum of all outflows from the steel mill for each resource:")
print(measure.flow(process_from=steel_mill)) # Option 4

print("---\n\n5) The sum of all outflows from the steel mill for steel:")
print(measure.flow(process_from=steel_mill, resource=steel)) # Option 5

print("---\n\n6) The sum of all inflows to the steel mill for every resource:")
print(measure.flow(process_to=steel_mill)) # Option 6

print("---\n\n7) The sum of all inflows to the wagon factory for steel:")
print(measure.flow(process_to=rail_wagon_factory, resource=steel)) # Option 7

print("---\n\n8) The sum of CO2 between the iron mine and the atmosphere:")
print(measure.flow(process_from=iron_mine, process_to=environment, resource=carbon_dioxide)) # Option 8

# .cumulative_resource()

# This example of cumulative_resource() is not yet operational
# See issue #16 on (https://github.com/dreamingspires/mat-dp-core) for details.
"""
print("---\n\n1) All cumulative resource flows from everything to everything:")
print(measure.cumulative_resource()) # Option 1

print("---\n\n2) All cumulative resource flows from everything to the wagon factory:")
print(measure.cumulative_resource(process=rail_wagon_factory)) # Option 2

print("---\n\n3) Cumulative resource flows of iron from everything to everything::")
print(measure.cumulative_resource(resource=iron)) # Option 3

print("---\n\n4) Cumulative amount of CO2 leading up to all the production rail factory:")
print(measure.cumulative_resource(process=rail_factory, resource=carbon_dioxide)) # Option 4
"""

mat_dp_core.measure¶

Objective Example Continued¶

Measure()¶

Methods¶

.run()¶

Option 1¶

Option 2¶

.resource()¶

Option 1¶

Option 2¶

Option 3¶

Option 4¶

.flow()¶

Option 1¶

Option 2¶

Option 3¶

Option 4¶

Option 5¶

Option 6¶

Option 7¶

Option 8¶

.cumulative_resource()¶

Option 1¶

Option 2¶

Option 3¶

Option 4¶

Example Code¶

`.run()`¶

`.resource()`¶

`.flow()`¶

`.cumulative_resource()`¶