An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models
Lcopt is a python module for creating fully parameterised LCA foreground models using a Flask based interactive GUI developed by James Joyce It integrates with SimaPro and BrightWay2
Online documentation, including full installation instructions, is available here
For lcopt to work you should have the latest version of brightway2 by Chris Mutel installed. This will make sure most of lcopts dependencies are satisfied.
The instructions for installing brightway below are current as of April 2017, but check here for the latest ones.
On the command line/console, create a new environment called lcopt:
conda create -n lcopt python=3.6
Then activate the lcopt environment using one of these:
# Mac/Linux
source activate lcopt
# Windows
activate lcopt
Then install brightway2:
conda install -y -q -c conda-forge -c cmutel -c haasad brightway2 jupyter
On windows there's an extra dependency:
conda install -y -q pywin32
Once brightway2 is ready to go, theres two more steps before installing lcopt itself...
Install pandas:
conda install -y -q pandas
Update werkzeug (this is a python 3.6 thing...):
pip install -U werkzeug
Finally, install lcopt via pip::
pip install lcopt
To analyse any of the models you create in lcopt in brightway, there's an extra installation step to set up the default project and databases.
Full details of this step are in the documentation
Lcopt can create models using external LCI data from the ecoinvent 3.3 cutoff database (ecoinvent license required) or the FORWAST database
Briefly, to set up lcopt to use ecoinvent 3.3:
Log into ecoinvent.org and go to the Files tab
Download the file called ecoinvent 3.3_cutoff_ecoSpold02.7z
Extract the file somewhere sensible on your machine, you might need to download 7-zip to extract the files.
Make a note of the folder path that contains the .ecospold files, its probably <path/extracted/to>/datasets/
Open a python console or jupyter notebook and use the setup utility function below:
from lcopt.utils import lcopt_bw2_setup
ecospold_path = r'path/to/ecospold/files' # put your own path in here
lcopt_bw2_setup(ecospold_path)
To set up lcopt to use FORWAST there's no download step (the utility function downloads the latest version of the database). Simply use:
from lcopt.utils import lcopt_bw2_forwast_setup
lcopt_bw2_forwast_setup()
Below are the basic commands to get lcopt's interactive GUI up and running to create your first model. More detailed instructions are available in the online documentation, including a video runthrough of creating a simple model using the ecoinvent 3.3 database.
Lcopt saves models in your current working directory, so before launching it, cd
to the folder you want to save your models in.
Lcopt is written in Python, so to use it open up a jupyter notebook or python shell and use the following commands
To import lcopt use
from lcopt import *
To create a model, you need to create an instance of the LcoptModel class using the model name as the first argument:
model = LcoptModel('My First Model')
By default the model will be populated in the background with the details to link to the ecoinvent 3.3 datasets. If you want your model to use FORWAST instead use:
model = LcoptModel('My First FORWAST Model', useForwast=True)
To load a model, make sure the file (*.lcopt) is in your working directory and use the model name (with or without the .lcopt extension) in this command:
model = LcoptModel(load='My First Model')
Note : If you accidentally forget to use load=
and you see a blank model don't panic. Lcopt won't overwrite your saved model unless you tell it to. Simply don't save the model and re-run the command with load=
To launch the GUI for your model simply call the launch_interact
method of your newly created model instance:
model.launch_interact()
This will start a Flask server and launch your web browser to access the GUI. If it doesn't or you accidentally close the GUI tab, simply go to http://127.0.0.1:5000/.
Information on how to use the GUI is located in 'More info...' panels dotted around at sensible locations within it.
For more details on using it, see the documentation or the video
If you have any problems, questions, comments, feature requests etc. please raise an issue here on github
If you want to contribute to Lcopt, you're more than welcome! Please fork the github repository and open a pull request.
Lcopt uses py.test and Travis for automated testing, so please accompany any new features with corresponding tests. See the tests
folder in the source code for examples.