Glossary

campaign

Collection of presample resources (ordered).

campaigns.db

Database in Brightway2 project used to manage presample ressources. Used by bw2preagg to store paths to base and balancing presample resources associated with a given samples_batch.

parallel_jobs

Argument used in LCI generation function to determine how many parallel jobs to run on different CPUs of one computer.

presamples package

Core data type of the presamples package. Folder containing data to inject in LCA matrices as well as matrix indices to identify where these data should be injected.

presamples resource

Data on a presamples package, savec in campaigns.db.

project

Brightway2 project, which is a self-contained, top-level container for LCI data, LCIA methods, etc. used in Brightway2.

Note: The argument project_name refers to the Brightway2 project used by bw2preagg.

project_name

Name of the Brightway2 project where the LCI database and LCIA methods were imported and that contains the campaigns.db.

ref_bio_dict

Dictionary containing biosphere matrix row indices for elementary flows.

samples_batch

A set of presamples, LCI arrays and LCIA arrays that are all generated from the same base data. Used to split out the work iteration-wise (e.g. calculate 5 batches of 1000 iterations rather than one batch of 5000 iterations). The total calculation time is not decreased, but it allows one to generate batches on different computers and makes results available, albeit perhaps with less iterations than required, more quickly.

slices

Subset of activity codes to treat as a set. Used when using computer clusters to generate LCI arrays.

Structure of the result_dir

├── common_files
│   ├── A_as_coo.xlsx (Row index, Col index, value for deterministic A matrix)
│   ├── A_as_coo_scipy.pickle (deterministic A matrix as SciPy sparse COO matrix)
│   ├── activity_dict.pickle
│   ├── B_as_coo.xlsx (Row index, Col index, value for deterministic B matrix)
│   ├── B_as_coo_scipy.pickle (deterministic B matrix as SciPy sparse COO matrix)
│   ├── bio_dict.pickle
│   ├── biosphere_description.xlsx (description of elementary flows in B matrix, per row index)
│   ├── cfs.npy (array with characterization factors, methods as columns and elementary flows as rows)
│   ├── cfs.xlsx (same as cfs.npy, but in Excel, with method names in columns)
│   ├── IO_Mapping.pickle
│   ├── ordered_activity_codes.json
│   ├── product_dict.pickle
│   └── technosphere_description.xlsx (description of products/activities in A matrix, per index)
├── presamples
│   ├── base_0 (base presamples package, samples_batch id=0)
│   ├── water_0 (water exchange balancing presamples package, samples_batch id=0)
│   ├── land_0 (land transformation exchange balancing presamples package, samples_batch id=0)
│   ├── base_1
│   ├── water_1
│   ├── land_1
│   └── ...
├── deterministic
│   ├── LCI
|   |   ├── 0 (sample_batch id = 0)
|   │   │   ├── activity_code_0.npy (rows=elementary flows, columns=iterations)
│   │   │   ├── activity_code_1.npy
│   │   │   ├── activity_code_2.npy
│   │   │   ├── ...
│   │   │   └── activity_code_n.npy
|   │   ├── 1 (sample_batch id)
│   │   │   ├── activity_code_0.npy
│   │   │   ├── activity_code_1.npy
│   │   │   ├── activity_code_2.npy
│   │   │   ├── ...
│   │   │   └── activity_code_n.npy
│   ├── LCIA
│   │   ├── method_abbrev_0
│   │   |   ├── totals
│   │   |   |   ├── 0 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   |   ├── 1 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   ├── per_exchange
│   │   |   |   ├── 0 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   |   ├── 1 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
├── probabilistic
│   ├── LCI
|   |   ├── 0 (sample_batch id = 0)
|   │   │   ├── activity_code_0.npy (rows=elementary flows, columns=iterations)
│   │   │   ├── activity_code_1.npy
│   │   │   ├── activity_code_2.npy
│   │   │   ├── ...
│   │   │   └── activity_code_n.npy
|   │   ├── 1 (sample_batch id)
│   │   │   ├── activity_code_0.npy
│   │   │   ├── activity_code_1.npy
│   │   │   ├── activity_code_2.npy
│   │   │   ├── ...
│   │   │   └── activity_code_n.npy
│   ├── LCIA
│   │   ├── method_abbrev_0
│   │   |   ├── totals
│   │   |   |   ├── 0 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   |   ├── 1 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   ├── per_exchange
│   │   |   |   ├── 0 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy
│   │   |   |   ├── 1 (sample_batch id)
│   │   |   │   │   ├── code_0.npy
│   │   |   │   │   ├── code_1.npy
│   │   |   │   │   ├── code_2.npy
│   │   |   │   │   ├── ...
│   │   |   │   │   └── code_n.npy