antelope-core 0.3.4.4

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core

Antelope Catalog - reference implementation.

This repository provides code that enables access to different forms of life cycle inventory and impact assessment data, ideally from both local and remote sources. It allows you to view and index data sources, inspect their contents, and perform exchange relation queries, quantity relation queries, and LCIA computations.

At present, the Antelope Catalog relies on local LCA data on your machine, just like other LCA software. However, the plan is to remove this requirement by off-loading computing requirements to the cloud.

Additional Packages

The software for constructing and inverting background matrices, which requires SciPy, is in a separate repository called antelope_background. The idea is that these computations can be performed remotely, allowing lightweight clients to run without scientific computing software (other than python). However, at the moment this is not yet available.

The antelope_foreground package allows users to construct and compute product models that use a mixture of data sources.

Please visit and install these packages to access and test these functions.

Quick Start

1. Configure a local catalog

antelope_core is on PyPI-- note the optional dependency if you want to access datasets in XML formats (ILCD, EcoSpoldV1, EcoSpoldV2):

user@host$ pip install antelope_core[XML]

Antelope stores its content in a catalog --- for automated unit testing, this should be specified in an environment variable:

user@host$ export ANTELOPE_CATALOG_ROOT=/path/to/where/you/want/catalog

Once that's done, the catalog can be "seeded" with a core set of free tools by running the local configuration unit test. This is a bit tricky because unit tests are not usually designed to be run on distributed code, so it requires a bit of a hack to specify the location of the installed package (note that if you are using a virtual environment, your site-packages directory is inside that virtual environment):

user@host$ python -m unittest discover -s /path/to/your/site-packages -p test_aa_local.py 

That will install: two different USLCI implementations (both somewhat stale), and the TRACI 2.1 LCIA methodology.

2. Start Running

You are now ready to perform LCIA calculations:

user@host$ python3
>>> from antelope_core import LcCatalog
>>> from antelope import enum  # a simple "enumerate-and-show items" for interactive use

If you have defined your catalog root in your environment, you can import it:

>>> from antelope_core.catalog.catalog_root import CATALOG_ROOT
>>> cat = LcCatalog(CATALOG_ROOT) 
Loading JSON data from /path/to/your/catalog/reference-quantities.json:
local.qdb: /path/to/your/catalog/reference-quantities.json
local.qdb: /data/GitHub/lca-tools/lcatools/qdb/data/elcd_reference_quantities.json
25 new quantity entities added (25 total)
6 new flow entities added (6 total)

Else, you can make it anything you want

>>> cat = LcCatalog('/path/to/anywhere') 
Loading JSON data from /path/to/anywhere/reference-quantities.json:
local.qdb: /path/to/anywhere/reference-quantities.json
local.qdb: /data/GitHub/lca-tools/lcatools/qdb/data/elcd_reference_quantities.json
25 new quantity entities added (25 total)
6 new flow entities added (6 total)

You then interact with the catalog by making queries to specific data sources:

>>> cat.show_interfaces()  # output shown after running `test_aa_local`
lcia.ipcc.2007.traci21 [basic, index, quantity]
local.lcia.traci.2.1 [basic, index, quantity]
local.qdb [basic, index, quantity]
local.uslci.ecospold [basic, exchange, quantity]
local.uslci.olca [basic, exchange, quantity]
 
>>> lcias = enum(cat.query('local.lcia.traci.2.1').lcia_methods())
local.lcia.traci.2.1: /data/LCI/TRACI/traci_2_1_2014_dec_10_0.xlsx
Loading workbook /data/LCI/TRACI/traci_2_1_2014_dec_10_0.xlsx
Applying stored configuration
Applying context hint local.lcia.traci.2.1:air => to air
Applying context hint local.lcia.traci.2.1:water => to water
Applying configuration to Traci21Factors with 11 entities at /data/LCI/TRACI/traci_2_1_2014_dec_10_0.xlsx
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Acidification Air
 [00] [local.lcia.traci.2.1] Acidification Air [kg SO2 eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Ecotoxicity, freshwater
 [01] [local.lcia.traci.2.1] Ecotoxicity, freshwater [CTUeco] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Eutrophication Air
 [02] [local.lcia.traci.2.1] Eutrophication Air [kg N eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Eutrophication Water
 [03] [local.lcia.traci.2.1] Eutrophication Water [kg N eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Global Warming Air
 [04] [local.lcia.traci.2.1] Global Warming Air [kg CO2 eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Human Health Particulates Air
 [05] [local.lcia.traci.2.1] Human Health Particulates Air [PM2.5 eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Human health toxicity, cancer
 [06] [local.lcia.traci.2.1] Human health toxicity, cancer [CTUcancer] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Human health toxicity, non-cancer
 [07] [local.lcia.traci.2.1] Human health toxicity, non-cancer [CTUnoncancer] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Ozone Depletion Air
 [08] [local.lcia.traci.2.1] Ozone Depletion Air [kg CFC-11 eq] [LCIA]
Missing canonical quantity-- adding to LciaDb
registering local.lcia.traci.2.1/Smog Air
 [09] [local.lcia.traci.2.1] Smog Air [kg O3 eq] [LCIA]
 
>>> lcias[3].show()
QuantityRef catalog reference (Eutrophication Water)
origin: local.lcia.traci.2.1
UUID: f07dbefc-a5a0-3380-92fb-4c5c8a82fabb
   Name: Eutrophication Water
Comment: 
==Local Fields==
           Indicator: kg N eq
          local_Name: Eutrophication Water
       local_Comment: 
local_UnitConversion: {'kg N eq': 1.0}
        local_Method: TRACI 2.1
      local_Category: Eutrophication Water
     local_Indicator: kg N eq
     
>>> _=enum(lcias[3].factors())
Imported 14 factors for [local.lcia.traci.2.1] Eutrophication Water [kg N eq] [LCIA]
 [00]   7.29 [GLO] [kg N eq / kg] local.lcia.traci.2.1/phosphorus: water (Eutrophication Water [kg N eq] [LCIA])
 [01]   3.19 [GLO] [kg N eq / kg] local.lcia.traci.2.1/phosphorus pentoxide: water (Eutrophication Water [kg N eq] [LCIA])
 [02]   2.38 [GLO] [kg N eq / kg] local.lcia.traci.2.1/phosphate: water (Eutrophication Water [kg N eq] [LCIA])
 [03]   2.31 [GLO] [kg N eq / kg] local.lcia.traci.2.1/phosphoric acid: water (Eutrophication Water [kg N eq] [LCIA])
 [04]  0.986 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitrogen: water (Eutrophication Water [kg N eq] [LCIA])
 [05]  0.779 [GLO] [kg N eq / kg] local.lcia.traci.2.1/ammonium: water (Eutrophication Water [kg N eq] [LCIA])
 [06]  0.779 [GLO] [kg N eq / kg] local.lcia.traci.2.1/ammonia: water (Eutrophication Water [kg N eq] [LCIA])
 [07]  0.451 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitric oxide: water (Eutrophication Water [kg N eq] [LCIA])
 [08]  0.291 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitrogen dioxide: water (Eutrophication Water [kg N eq] [LCIA])
 [09]  0.291 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitrogen oxides: water (Eutrophication Water [kg N eq] [LCIA])
 [10]  0.237 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitrate: water (Eutrophication Water [kg N eq] [LCIA])
 [11]  0.227 [GLO] [kg N eq / kg] local.lcia.traci.2.1/nitric acid: water (Eutrophication Water [kg N eq] [LCIA])
 [12]   0.05 [GLO] [kg N eq / kg] local.lcia.traci.2.1/biological oxygen demand: water (Eutrophication Water [kg N eq] [LCIA])
 [13]   0.05 [GLO] [kg N eq / kg] local.lcia.traci.2.1/chemical oxygen demand: water (Eutrophication Water [kg N eq] [LCIA])
 
>>>

Specific objects, whose IDs are known, can be retrieved by ID:

>>> p = cat.query('local.uslci.olca').get('ba5df01a-626b-35b8-859f-f1df42dd54a0')
...
 
>>> p.show()
ProcessRef catalog reference (ba5df01a-626b-35b8-859f-f1df42dd54a0)
origin: local.uslci.olca
UUID: ba5df01a-626b-35b8-859f-f1df42dd54a0
   Name: Polyethylene, low density, resin, at plant, CTR
Comment: 
==Local Fields==
   SpatialScope: RNA
  TemporalScope: {'begin': '2002-01-01-05:00', 'end': '2003-01-01-05:00'}
Classifications: ['Chemical Manufacturing', 'All Other Basic Organic Chemical Manufacturing']

>>> rxs = enum(p.references())
 [00] [ Polyethylene, low density, resin, at plant, CTR [RNA] ]*==>  1 (kg) Polyethylene, low density, resin, at plant, CTR 
 [01] [ Polyethylene, low density, resin, at plant, CTR [RNA] ]*==>  0.429 (MJ) Recovered energy, for Polyethylene, low density, resin, at plant, CTR
 
>>> 

LCIA can be computed for process inventories (note, however, that without antelope_background it is not possible to compute LCI results. In this case the cradle-to-resin dataset is already an LCI). Again, to do that, please visit / install antelope_background.

>>> res = lcias[3].do_lcia(p.inventory(rxs[0]))
...

>>> res.show_details()
[local.lcia.traci.2.1] Eutrophication Water [kg N eq] [LCIA] kg N eq
------------------------------------------------------------

[local.uslci.olca] Polyethylene, low density, resin, at plant, CTR [RNA]:
   1.14e-05 =       0.05  x   0.000228 [GLO] local.lcia.traci.2.1/chemical oxygen demand, water, unspecified
   5.89e-06 =      0.779  x   7.55e-06 [GLO] local.lcia.traci.2.1/ammonia, water, unspecified
   2.85e-06 =       0.05  x    5.7e-05 [GLO] local.lcia.traci.2.1/biological oxygen demand, water, unspecified
   7.29e-07 =       7.29  x      1e-07 [GLO] local.lcia.traci.2.1/phosphorus, water, unspecified
   7.62e-08 =      0.986  x   7.73e-08 [GLO] local.lcia.traci.2.1/nitrogen, water, unspecified
   2.42e-08 =      0.779  x    3.1e-08 [GLO] local.lcia.traci.2.1/ammonium, water, unspecified
   2.1e-05 [local.lcia.traci.2.1] Eutrophication Water [kg N eq] [LCIA]
   
>>>

Search requires an index to be created:

>>> q = cat.query('local.uslci.olca')
>>> _=enum(q.processes(Name='polyethylene')
---------------------------------------------------------------------------
IndexRequired                             Traceback (most recent call last)
...
IndexRequired: itype index required for attribute processes | ()

>>> cat.index_ref(q.origin)
...
'local.uslci.olca.index.20210205'

>>> _=enum(q.processes(Name='polyethylene')
 [00] [local.uslci.olca] Polyethylene, low density, resin, at plant [RNA]
 [01] [local.uslci.olca] Polyethylene, linear low density, resin, at plant [RNA]
 [02] [local.uslci.olca] Polyethylene terephthalate, resin, at plant [RNA]
 [03] [local.uslci.olca] Polyethylene, linear low density, resin, at plant, CTR [RNA]
 [04] [local.uslci.olca] Polyethylene, low density, resin, at plant, CTR [RNA]
 [05] [local.uslci.olca] Polyethylene, high density, resin, at plant, CTR [RNA]
 [06] [local.uslci.olca] Polyethylene, high density, resin, at plant  [RNA]
 [07] [local.uslci.olca] Polyethylene terephthalate, resin, at plant, CTR [RNA]
 
>>>

Installing Ecoinvent

If you have an ecoinvent license, you can install it in your catalog by first downloading the 7z files that contain the EcoSpold datasets and storing them on your system.

You will need to create a folder for ecoinvent, and then create a subfolder for each version (say, '3.7.1'), and put the 7z files in that.

user@host$ mkdir -p /path/to/Ecoinvent/3.7.1

The 7z files unfortunately need to be extracted before they can be loaded. After you are done you should have something that looks like this:

user@host$ ls /path/to/Ecoinvent/3.7.1
'ecoinvent 3.7.1_cutoff_ecoSpold02'  'ecoinvent 3.7.1_cutoff_ecoSpold02.7z'
user@host$ 

After that, you can setup ecoinvent in your catalog from within python:

>>> from antelope_core.data_sources.ecoinvent import EcoinventConfig
>>> ec = EcoinventConfig('/path/to/Ecoinvent')
>>> _=enum(ec.origins)
 [00] local.ecoinvent.3.7.1.cutoff
 
>>> ec.register_all_resources(cat)
>>> 

Again, you will need to index the resources before being able to search through them- this takes several minutes. This is why we are working on a remote solution for this problem.

Warning: if you want to do Ecoinvent LCI as well, you will need antelope_background -- please visit that page.

Contributing

Fork, open an issue, whatever.