Metadata-Version: 1.1
Name: lcopt-dev
Version: 0.4.2
Summary: An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models
Home-page: https://github.com/pjamesjoyce/lcopt/
Author: P. James Joyce
Author-email: pjamesjoyce@gmail.com
License: Copyright 2017 P. James Joyce

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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Download-URL: https://github.com/pjamesjoyce/lcopt/archive/0.4.2.tar.gz
Description-Content-Type: UNKNOWN
Description: # lcopt-dev
        Development branch for the interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models (lcopt)
        
        | 	Platform		| Status 		|
        |---------------|---------------|
        | Unix     		|  [![Build Status](https://travis-ci.org/pjamesjoyce/lcopt.svg?branch=development)](https://travis-ci.org/pjamesjoyce/lcopt) |
        | Windows  		|  [![Windows Build status](https://ci.appveyor.com/api/projects/status/vwtfm3ml7hy070ku/branch/development?svg=true)](https://ci.appveyor.com/project/pjamesjoyce/lcopt/branch/development)   |
        | Test coverage | [![Coverage Status](https://coveralls.io/repos/github/pjamesjoyce/lcopt/badge.svg?branch=development)](https://coveralls.io/github/pjamesjoyce/lcopt?branch=development) |
        |Citation | [![Publication](http://joss.theoj.org/papers/c0b544bee185c9ac75e96d24b8573547/status.svg)](http://joss.theoj.org/papers/c0b544bee185c9ac75e96d24b8573547)|
        
        Lcopt is a python module for creating fully parameterised LCA foreground models using a Flask based interactive GUI developed by [James Joyce](https://pjamesjoyce.github.io/)
        
        It integrates directly with BrightWay2, and can export models to SimaPro
        
        You can export and share your model and results as an .lcoptview file, and share them with a wider audience using [lcoptview](http://lcoptview.rtfd.io)
        
        Online documentation, including full installation instructions, is available [here](http://lcopt.readthedocs.io)
        
        ## Installation
        
        ### Development installation
        
        Lcopt is continuing to develop, with new features and extensions being added. The `lcopt-dev` conda package is updated each time an update is pushed to the [`development` branch on github](https://github.com/pjamesjoyce/lcopt/tree/development).
        
        It's recommended that you create a new environment separate from the one with `lcopt` in to install `lcopt-dev`, as they will overwrite one another.
        
        Create and activate a dev environment at the command line/terminal like this
        
        ```
        conda create -y -n lcopt-dev -c conda-forge -c cmutel -c haasad -c pjamesjoyce lcopt-dev
        activate lcopt-dev    # Note, use "source activate lcopt-dev" on Unix
        ```
        
        Or to get the most up to date version of `lcopt-dev` in your existing environment:
        
        ```
        activate lcopt-dev    # Note, use "source activate lcopt-dev" on Unix
        conda update -c conda-forge -c cmutel -c haasad -c pjamesjoyce lcopt-dev
        ```
        
        ### Linking lcopt to brightway
        
        `lcopt-dev` makes this easy, by using the excellent [`eidl`](https://github.com/haasad/EcoInventDownLoader) developed by [Adrian Haas](https://github.com/haasad).
        
        Lcopt-dev can create models using external LCI data from any version of the [ecoinvent database](http://www.ecoinvent.org/database/ecoinvent-33/ecoinvent-33.html) (ecoinvent license required) or the [FORWAST database](http://forwast.brgm.fr/)
        
        If you have an ecoinvent login, open up the command line/console and type:
        
        ```
        lcopt-settings
        ```
        
        This will launch the settings page in your web browser. Enter your username and password, and choose your preferred version of ecoinvent. Click save.
        
        ![Lcopt settings](./docs/assets/lcopt_dev_settings.jpg)
        
        The first time you create a model, lcopt will automatically download and set up the ecoinvent database in brightway - just be patient as it takes a while. This only needs to happen once though.
        
        If you don't have an ecoinvent login, leave this blank and lcopt will automatically download and use FORWAST instead.
        
        ## Using lcopt (Easy)
        
        Below are the basic commands to get lcopt's interactive GUI up and running to create your first model. A [Getting Started](https://lcopt.readthedocs.io/en/latest/2_Getting_Started.html) guide, including a [video runthrough](https://lcopt.readthedocs.io/en/latest/2_Getting_Started.html#video), as well as [more detailed instructions](https://lcopt.readthedocs.io/en/latest/3_use.html) are available in the [online documentation](https://lcopt.readthedocs.io/).
        
        To launch lcopt and view an example model, at the command line use:
        
        ```
        lcopt-launcher
        ```
        
        Lcopt is written in Python, so you can also use it from within a Python shell. Open up a jupyter notebook or python shell and use the following commands:
        
        ## Using Lcopt from the python shell/Jupyter notebooks (Recommended)
        
        To import lcopt use: 
        
        ```python
        from lcopt import *
        ```
        
        ### Creating a new model
        
        To create a model, you need to create an instance of the LcoptModel class using the model name as the first argument:
        
        ```python
        model = LcoptModel('My First Model')
        ```
        
        By default the model will be populated in the background with the details to link to the version of ecoinvent you specify in settings. If you want your model to use a dirfferent version or system model you can specify this using:
        
        
        ```python
        model = LcoptModel('My First Ecoinvent 3.4 APOS Model', ecoinvent_version="3.4", ecoinvent_system_model="apos")
        ```
        
        Or if you want to use FORWAST instead use:
        
        ```python
        model = LcoptModel('My First FORWAST Model', useForwast=True)
        ```
        
        ### Loading an existing model
        
        Lcopt saves models in your local APPDATA directory by default. In windows this is C:\Users\[your_name]\AppData\Local\Lcopt\Lcopt.
        
        You can optionally tell lcopt to save models in your current directory using `lcopt-settings`
        
        To load a model, use the model name (with or without the .lcopt extension) or the full file path (if it's not in the current directory or defualt directory), in this command:
        
        ```python
        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=`
        
        ### Launching the GUI
        
        To launch the GUI for your model simply call the `launch_interact` method of your newly created model instance:
        
        ```python
        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/](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](https://lcopt.readthedocs.io/en/latest/3_use.html) or the [video](https://lcopt.readthedocs.io/en/latest/2_Getting_Started.html#video)
        
        ## Contribute
        
        If you have any problems, questions, comments, feature requests etc. please [raise an issue here on github](https://github.com/pjamesjoyce/lcopt/issues)
        
        If you want to contribute to Lcopt, you're more than welcome! Please fork the [github repository](https://github.com/pjamesjoyce/lcopt/) and open a pull request. 
        
        Lcopt uses [py.test](https://docs.pytest.org/en/latest/index.html>) and Travis for automated testing, so please accompany any new features with corresponding tests. See the `tests` folder in the [source code](https://github.com/pjamesjoyce/lcopt/tree/master/tests) for examples.  
        
        ## Cite
        
        Lcopt has been published in the [Journal of Open Source Software](http://joss.theoj.org/papers/10.21105/joss.00339).
        
        [![Publication](http://joss.theoj.org/papers/c0b544bee185c9ac75e96d24b8573547/status.svg)](http://joss.theoj.org/papers/c0b544bee185c9ac75e96d24b8573547)
        
        You can download the citation in BibTeX for use in LaTeX or importing into [Mendeley](https://www.mendeley.com/)/other reference management software from [here](http://www.doi2bib.org/#/doi/10.21105/joss.00339).
        
        Or use the following citation:
        
        Joyce, P.J., 2017. Lcopt - An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models. Journal of Open Source Software, 2:16. doi:10.21105/joss.00339
        
        ## Thanks
        
        Thanks to [Adrian Haas](https://github.com/haasad) for contributing to the ongoing development of lcopt!
        
Keywords: LCA,Life Cycle Assessment,Foreground system,Background system,Foreground model,Fully parameterised
Platform: UNKNOWN
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
