A Python package for calling openLCA functions from Python.
Not all features and bug-fixes are currently available on the version on PyPi.org. If you want to use the latest development version, just install it directly from the master branch, e.g. with pip:
pip install -U git+https://github.com/GreenDelta/olca-ipc.py.git/@master
openLCA provides an implementation of an JSON-RPC based protocol for inter-process communication (IPC). With this, it is possible to call functions in openLCA and processing their results outside of openLCA. The olca-ipc package provides a convenience API for using this IPC protocol from standard Python (Cpython v3.9+) so that it is possible to use openLCA as a data storage and calculation engine and combine it with the libraries from the Python ecosystem (numpy, pandas and friends).
The openLCA IPC protocol is based on the openLCA data exchange format which is specified in the olca-schema repository. The olca-ipc package provides a class based implementation of the openLCA data exchange format and an API for communicating with an openLCA IPC server via instances of these classes.
The current stable version of olca-ipc is available via the Python Package Index. Thus, in order to use it, you can just install (and uninstall) it with pip:
pip install -U olca-ipc
If you want to use the current development branch you can download it from Github and install it from the extracted folder:
# optionally, first uninstall it # pip uninstall olca-ipc cd folder/where/you/extracted/the/zip pip install .
In order to communicate with openLCA, you first need to start an openLCA IPC server. You can do this via the user interface in openLCA under Window > Developer Tools > IPC Server. The IPC server runs on a specific port, e.g. 8080, to which you connect from an IPC client:
import olca client = olca.Client(8080)
An instance of the olca.Client class is then a convenient entry point for calling functions of openLCA and processing their results. The following examples show some typical uses cases (note that these are just examples without input checks, error handling, code structuring, and all the things you would normally do).
openLCA provides different types of calculations which can be selected via the calculation_type in a calculation setup. In the following example, a calculation setup with a product system and impact assessment method is created, calculated, and finally exported to Excel:
import olca client = olca.Client(8080) # create the calculation setup setup = olca.CalculationSetup() # define the calculation type here # see http://greendelta.github.io/olca-schema/html/CalculationType.html setup.calculation_type = olca.CalculationType.CONTRIBUTION_ANALYSIS # select the product system and LCIA method setup.impact_method = client.find(olca.ImpactMethod, 'TRACI 2.1') setup.product_system = client.find(olca.ProductSystem, 'compost plant, open') # amount is the amount of the functional unit (fu) of the system that # should be used in the calculation; unit, flow property, etc. of the fu # can be also defined; by default openLCA will take the settings of the # reference flow of the product system setup.amount = 1.0 # calculate the result and export it to an Excel file result = client.calculate(setup) client.excel_export(result, 'result.xlsx') # the result remains accessible (for exports etc.) until # you dispose it, which you should always do when you do # not need it anymore client.dispose(result)
Parameterized calculation setups
In order to calculate a product system with different parameter sets, you can pass a set of parameter redefinitions directly with a calculation setup into a calculation. With this, you do not need to modify a product system or the parameters in a database in order to calculate it with different parameter values:
# ... same steps as above setup = olca.CalculationSetup() # ... for something in your.parameter_data: redef = olca.ParameterRedef() redef.name = the_parameter_name redef.value = the_parameter_value # redef.context = ... you can also redefine process and LCIA method # parameters by providing a parameter context which # is a Ref (reference) to the respective process or # LCIA method; with no context a global parameter is # redefined setup.parameter_redefs.append(redef)
As the name says, a parameter redefinition redefines the value of an existing global, process, or LCIA method parameter.
Running Monte-Carlo simulations is similar to normal calculations but instead of calculate you call the simulator method which will return a reference to a simulator which you then use to run calculations (where in each calculation the simulator generates new values for the uncertainty distributions in the system). You get the result for each iteration and can also export the result of all iterations later to Excel. As for the results of the normal calculation, the the simulator should be disposed when it is not used anymore:
import olca client = olca.Client(8080) # creating the calculation setup setup = olca.CalculationSetup() setup.calculation_type = olca.CalculationType.MONTE_CARLO_SIMULATION setup.impact_method = client.find(olca.ImpactMethod, 'TRACI 2.1') setup.product_system = client.find(olca.ProductSystem, 'compost plant') setup.amount = 1.0 # create the simulator simulator = client.simulator(setup) for i in range(0, 10): result = client.next_simulation(simulator) first_impact = result.impact_results print('iteration %i: result for %s = %4.4f' % (i, first_impact.impact_category.name, first_impact.value)) # we do not have to dispose the result here (it is not cached # in openLCA); but we need to dispose the simulator later (see below) # export the complete result of all simulations client.excel_export(simulator, 'simulation_result.xlsx') # the result remains accessible (for exports etc.) until # you dispose it, which you should always do when you do # not need it anymore client.dispose(simulator)
For more information and examples see the package documentation
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