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Discussionįour distinct steps were identified in the broader problem of automating PSM construction: creating a foreground model from product data, determining the quantitative properties of foreground model flows, linking flows to background datasets, and expressing the linked model in a format that could be used by existing LCA software. Another approach provided an interactive web application for matching product components to standardized product classification systems to facilitate characterization and linking. Another approach focused on producing a flexible description of the model structure that removed redundancy and permitted aggregation. One approach used semantic similarity relations to identify best-fit background datasets. ResultsĮach developer took a distinct approach to the problem.
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The resulting prototypes were compared and tested with additional product specifications.
#OPENLCA PYTHON SOFTWARE#
The participants were given a confidential product specification in the form of a Bill of Materials (BOM) and were asked to develop and test prototype software under a limited time period that converted the BOM into a foreground model and linked it with one or more a background datasets, along with a list of other functional requirements.
#OPENLCA PYTHON HOW TO#
Three experienced LCA software developers were commissioned to independently develop software prototypes to address the problem of how to generate an operable PSM from a complex product specification. To relieve the burden on the practitioner to create the linkages and reduce bias, this study aimed at applying automation to create foreground LCI from primary data and link it to background data to construct product system models (PSM). Please provide informationĪbout the open source projects you own / you use.The flexibility of life cycle inventory (LCI) background data selection is increasing with the increasing availability of data, but this comes along with the challenge of using the background data with primary life cycle inventory data. Open source products are scattered around the web. We have large collection of open source products. Inliner - Node utility to inline images, CSS and JavaScript for a web page - useful for mobile sitesĮditor.md - The open source embeddable online markdown editor (component). Sequelize-json-schema - Use your Sequelize models in JSON Schemas or Swaggerĭecimal.js - An arbitrary-precision Decimal type for JavaScript
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#OPENLCA PYTHON ANDROID#
LollipopShowcase - A simple app to showcase Androids Material Design and some of the cool new cool stuff in Android Lollipopĭiscord-image-downloader-go - A simple tool which downloads pictures posted in discord channels of your choice to a local folder Rustodon - A Mastodon-compatible, ActivityPub-speaking server in RustĪkroma-wallet-desktop - Desktop Wallet for the Akroma Network
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HandySwift - Handy Swift features that didn't make it into the Swift standard library. Openlca-python-tutorial - Explains the usage of the openLCA API from Python (Jython) Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. He originally hails from Vancouver, BC and received his Ph.D. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. Statistical-analysis-python-tutorial - Statistical Data Analysis in PythonĬhris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine.