- Importing Ecoinvent Database In Ecospold Format For Mac Windows 10
- Importing Ecoinvent Database In Ecospold Format For Mac Computer
- Importing Ecoinvent Database In Ecospold Format For Mac Pdf
- Importing Ecoinvent Database In Ecospold Format For Mac Windows 7
Dear everyone,I glanced the Ecospold 02 documentation and read that the @contextId is a required field for Elementary and Intermediate Exchanges MasterData files. But in the v3.1 Ecoinvent DB, there is no defined @contextId in the ElementaryExchanges.xml and IntermediateExchanges.xml in MasterData. It requires the path to the ecospold files containing the Ecoinvent 3.3 cutoff database. If you don’t have these files, log into ecoinvent.org and go to the Files tab. Download the file called ecoinvent 3.3cutoffecoSpold02.7z. Extract the file somewhere sensible on your machine, you might need to download 7-zip to extract the files. ISO-14048 compliant data formats: EcoSpold v.1. ecoinvent database, broadly supported as exchange format. 8-9-17 LCA Databases Helpdesk 12. EcoSpold (v1) limitations. No support for parameters in data sets. Only two languages. No (real) distinction betweeen process and flow/product. Gabi software.
Importing Ecoinvent Database In Ecospold Format For Mac Windows 10
Overview of Ecoinvent
Jan 08, 2018 But I am confused when I want to import the entire Ecoinvent database, is it a must to apply for the license from the openLCA Nexus again? Or, when asked to confirm the order, can I sent the same confirmation that I once used on Ecoinvent website as a substitution? (Although the format is not the same, the included information is much similar).
Ecoinvent database comprises LCI data from the energy, transport, building materials, chemicals, paper and pulp, waste treatment and agricultural sectors reflecting the production and supply situation in the year 2000, based on the Swiss and European demand patterns. The major applicability is in a European context, but selected data sets (such as oil production in Nigeria or natural gas production in Russia) have a broader international application.
It sums up around 4000 industry based data sets, providing the user with information from all of the following areas:
- energy supply
- resource extraction
- material supply (building materials packaging materials, metals)
- metal processing
- chemicals
- electronics
- agriculture
- waste management services
- transport services
The official website of Ecoinvent database is here.
Main components of ecoinvent database system
Main components of ecoinvent database system | |
---|---|
Name of component | Function |
Central database | The central database contains Life Cycle Inventory data on energy systems, transport systems, waste treatment systems, chemicals, building materials,etc., and Life Cycle Impact Assessment (LCIA) methods such as the Swiss Ecological Scarcity, Ecoindicator 99, IMPACT 2002C or the CML characterisation scheme 2001. The database is located on a computer server and accessible via the Internet. |
Calculation routines | Data are supplied by the partner institutes as non-terminated unit processes (i.e., they can and usually do contain exchanges from and into the technosphere as well as elementary flows). The computation of cumulative inventory results is performed with powerful calculation routines related to the central database. Unit process raw data as well as LCI results include (cumulative) uncertainty ranges. |
Editor | The local database administrators of the participating institutes use the editor to create new data sets and to change, complete or delete existing data sets. The editor administrates the data set names (via a direct link to the central database, where the index of data set names is located), ensures the use of the actual list of names when compiling new inventories and includes a unit converter. The editor acts as the interface between the local administrator and the central database and generates files in the ecoinvent data format. |
Administration tools | The administration tool supports the integration of data sets delivered by the cooperating institutes into the central database. It helps to verify the completeness of data sets, calculates inventories and (normalised and weighted, if appropriate) category indicator results and supports the administration of ecoinvent users. |
Query tool | The Query tool is the users’ interface to the database and is used to download data sets from the central database. It enables the search for individual processes, for processes of a certain economic sector (e.g., transport or energy sector) or for data from a certain institute. General information (so-called metainformation) on the processes (technology, age of data, geographic coverage, etc.) is accessible to everyone, whereas the quantitative LCI data is only accessible for registered ecoinvent members (customers). |
Data (exchange) format | The data exchange format lists all data fields that were available to describe a data set. It has evolved from the international SPOLD data exchange formatand corresponds to the international technical specification ISO/TS 14048. Some of the data fields are mandatory, i.e. information must be provided. Among other features, the data exchange format allows for specifying upper and lower estimates (or the 95% standard deviation) as well as the probability distribution(e.g., lognormal) of inventory data. |
Local databases | Commercially available LCA-software such as Emis, PEMS, Regis, SimaPro, TEAM and Umberto are used as local databases. These local databasesare tailored for an implementation of ecoinvent data v1.0 and its updates. It is recommended to use the ecoinvent data (exchange) format for the purpose of data import. |
Data exchange and XML-technology
- Data set documentation
A process, its products and its life cycle inventory data are documented using the ecoinvent data format (EcoSpold) with the following structure:
The structure of ecoinvent data format | |
Process | ReferenceFunction, defining the product or service output to which all emissions and requirements are referred. |
TimePeriod, defining the temporal validity of the data set. | |
Geography, defining the geographical validity of the data set. | |
Technology, describing the technology(ies) of the process. | |
DataSetInformation, defining the kind of process or product system, and the version number of the data set. | |
Modeling and validation | Representativeness, defining the representativeness of the data used. |
Sources, listing the literature and publications used in a data set. | |
Validations, listing the reviewers and their comments. | |
Administrative information | DataEntryBy, documenting the person in charge of implementing the data set in the database. |
DataGeneratorAndPublication, documenting the originator and the published source of the data set. | |
Persons, listing complete addresses of all persons mentioned in a data set. | |
Flow data | Exchanges, quantifying all inputs from technical systems and nature to the process and all outputs from the process to nature and to other technical systems |
Allocations, describing and quantifying allocation procedures and factors, respectively, required for multi-function processes. |
- Role of XML-technology
Once a data set is chosen for download, one or several data sets are converted to one XML-file (extended markup language) and saved on the local computer. XML schemes facilitate data exchange between different LCA-databases and -software. It can easily be extended by LCA-softwarespecific requirements and upwards and downwards compatibility poses no major problems.
For a flexible application of a data exchange between local LCA-software tools and the central database, a data exchange format in XML-technology is used. Forthat purpose XML schemes are applied. Although Document Type Definitions (DTD) are more widespread nowadays, they will most likely be substituted by schemes. On the one hand, schemes have a much higher performance and, on the other, they themselves use the XML language (as opposite to DTD, which uses its own specific language).
Schemes are used for validation and for documentation purposes. The scheme provides information on the general structure of an ecoinvent data set. Furthermore,all elements of a scheme may be completed with documentation information and comments.
Latest version Released:
An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models
Project description
# lcopt
An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models
[![Build Status](https://travis-ci.org/pjamesjoyce/lcopt.svg?branch=master)](https://travis-ci.org/pjamesjoyce/lcopt)
[![Coverage Status](https://coveralls.io/repos/github/pjamesjoyce/lcopt/badge.svg?branch=master)](https://coveralls.io/github/pjamesjoyce/lcopt?branch=master)
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 with SimaPro and BrightWay2
Online documentation, including full installation instructions, is available [here](http://lcopt.readthedocs.io)
## Installation
### Basic Installation
For lcopt to work you should have the latest version of [brightway2](https://brightwaylca.org/) 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](https://docs.brightwaylca.org/installation.html) 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
```
### Linking lcopt to brightway
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](https://lcopt.readthedocs.io/en/latest/1_installation.html#setting-up-brightway2-for-lcopt)
Lcopt can create models using external LCI data from the [ecoinvent 3.3 cutoff database](http://www.ecoinvent.org/database/ecoinvent-33/ecoinvent-33.html) (ecoinvent license required) or the [FORWAST database](http://forwast.brgm.fr/)
Briefly, to set up lcopt to use ecoinvent 3.3:
Log into [ecoinvent.org](http://www.ecoinvent.org/login-databases.html) 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](http://www.7-zip.org/download.html) 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:
```python
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:
```python
from lcopt.utils import lcopt_bw2_forwast_setup
lcopt_bw2_forwast_setup()
```
## Example Usage
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](https://lcopt.readthedocs.io/en/latest/2_use.html), including a [video runthrough](https://lcopt.readthedocs.io/en/latest/3_video_runthrough.html) 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
### Importing Lcopt
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 ecoinvent 3.3 datasets. If you want your model to use FORWAST instead use:
```python
model = LcoptModel('My First FORWAST Model', useForwast=True)
```
### Loading an existing model
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:
```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/2_use.html) or the [video](https://lcopt.readthedocs.io/en/latest/3_video_runthrough.html)
## 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.
An interactive tool for creating fully parameterised Life Cycle Assessment (LCA) foreground models
[![Build Status](https://travis-ci.org/pjamesjoyce/lcopt.svg?branch=master)](https://travis-ci.org/pjamesjoyce/lcopt)
[![Coverage Status](https://coveralls.io/repos/github/pjamesjoyce/lcopt/badge.svg?branch=master)](https://coveralls.io/github/pjamesjoyce/lcopt?branch=master)
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 with SimaPro and BrightWay2
Online documentation, including full installation instructions, is available [here](http://lcopt.readthedocs.io)
## Installation
### Basic Installation
For lcopt to work you should have the latest version of [brightway2](https://brightwaylca.org/) 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](https://docs.brightwaylca.org/installation.html) 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
```
### Linking lcopt to brightway
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](https://lcopt.readthedocs.io/en/latest/1_installation.html#setting-up-brightway2-for-lcopt)
Lcopt can create models using external LCI data from the [ecoinvent 3.3 cutoff database](http://www.ecoinvent.org/database/ecoinvent-33/ecoinvent-33.html) (ecoinvent license required) or the [FORWAST database](http://forwast.brgm.fr/)
Briefly, to set up lcopt to use ecoinvent 3.3:
Log into [ecoinvent.org](http://www.ecoinvent.org/login-databases.html) 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](http://www.7-zip.org/download.html) 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:
```python
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:
```python
from lcopt.utils import lcopt_bw2_forwast_setup
lcopt_bw2_forwast_setup()
```
## Example Usage
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](https://lcopt.readthedocs.io/en/latest/2_use.html), including a [video runthrough](https://lcopt.readthedocs.io/en/latest/3_video_runthrough.html) 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
### Importing Lcopt
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 ecoinvent 3.3 datasets. If you want your model to use FORWAST instead use:
```python
model = LcoptModel('My First FORWAST Model', useForwast=True)
```
### Loading an existing model
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:
```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/2_use.html) or the [video](https://lcopt.readthedocs.io/en/latest/3_video_runthrough.html)
## 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.
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