Note
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 v3.6+) 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-ipcIf 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).
The olca package contains a class model with type annotations for the
olca-schema model that is used
for exchanging data with openLCA. With the type annotations you should get good
editor support (type checks and IntelliSense). You can create, update
and link data models as defined in the openLCA schema (e.g. as for
processes,
flows, or
product systems).
(Note that we convert camelCase names like calculationType of attributes and
functions to lower_case_names_with_underscores like calculation_type when
generating the Python API).
The olca.Client class provides methods like get, find, insert,
update, and delete to work with data. The following example shows how to
create a new flow and link it to an existing flow property with the name Mass:
import olca
import uuid
client = olca.Client(8080)
# find the flow property 'Mass' from the database
mass = client.find(olca.FlowProperty, 'Mass')
# create a flow that has 'Mass' as reference flow property
steel = olca.Flow()
steel.id = str(uuid.uuid4())
steel.flow_type = olca.FlowType.PRODUCT_FLOW
steel.name = "Steel"
steel.description = "Added from the olca-ipc python API..."
# in openLCA, conversion factors between different
# properties/quantities of a flow are stored in
# FlowPropertyFactor objects. Every flow needs at
# least one flow property factor for its reference
# flow property.
mass_factor = olca.FlowPropertyFactor()
mass_factor.conversion_factor = 1.0
mass_factor.flow_property = mass
mass_factor.reference_flow_property = True
steel.flow_properties = [mass_factor]
# save it in openLCA, you may have to refresh
# (close & reopen the database to see the new flow)
client.insert(steel)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)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[0]
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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