`bw2data.backends`#

Submodules#

Package Contents#

Classes#

`Activity`	A MutableMapping is a generic container for associating
`ActivityDataset`
`Database`	A base class for SQLite backends.
`Exchange`	A MutableMapping is a generic container for associating
`ExchangeDataset`
`SubstitutableDatabase`

Functions#

get_id(key)

Attributes#

`Edge`
`Node`
`SQLiteBackend`
`projects`
`sqlite3_lci_db`

class bw2data.backends.Activity(document=None, **kwargs)[source]#

Bases: bw2data.proxies.ActivityProxyBase

Inheritance diagram of bw2data.backends.Activity

A MutableMapping is a generic container for associating key/value pairs.

This class provides concrete generic implementations of all methods except for __getitem__, __setitem__, __delitem__, __iter__, and __len__.

Create an Activity proxy object.

If this is a new activity, can pass kwargs.

If the activity exists in the database, document should be an ActivityDataset.

property id#

property key#

_change_code(new_code)#

_change_database(new_database)#

biosphere()#

consumers(kinds=('technosphere', 'generic consumption'))#

copy(code=None, **kwargs)#

Copy the activity. Returns a new Activity.

code is the new activity code; if not given, a UUID is used.

kwargs are additional new fields and field values, e.g. name=’foo’

delete()#

edges()#

exchanges()#

new_edge(**kwargs)#: Create a new exchange linked to this activity

new_exchange(**kwargs)#

producers()#

production(include_substitution=False)#

rp_exchange()#

Return an Exchange object corresponding to the reference production. Uses the following in order:

The production exchange, if only one is present
The production exchange with the same name as the activity reference product.

Raises ValueError if no suitable exchange is found.

save()#

substitution()#

technosphere(include_substitution=False)#

upstream(kinds=('technosphere', 'generic consumption'))#

class bw2data.backends.ActivityDataset[source]#

Bases: peewee.Model

Inheritance diagram of bw2data.backends.ActivityDataset

property key#

code#

data#

database#

location#

name#

product#

type#

class bw2data.backends.Database(name=None, *args, **kwargs)[source]#

Bases: peewee.Model

Inheritance diagram of bw2data.backends.Database

A base class for SQLite backends.

Subclasses must support at least the following calls:

load()
write(data)

In addition, they should specify their backend with the backend attribute (a unicode string).

rename
copy
find_dependents
random
process

For new classes to be recognized by the DatabaseChooser, they need to be registered with the config object, e.g.:

config.backends['backend type string'] = BackendClass

Instantiation does not load any data. If this database is not yet registered in the metadata store, a warning is written to stdout.

The data schema for databases in voluptuous is:

exchange = {
        Required("input"): valid_tuple,
        Required("type"): basestring,
        }
exchange.update(uncertainty_dict)
lci_dataset = {
    Optional("categories"): Any(list, tuple),
    Optional("location"): object,
    Optional("unit"): basestring,
    Optional("name"): basestring,
    Optional("type"): basestring,
    Optional("exchanges"): [exchange]
}
db_validator = Schema({valid_tuple: lci_dataset}, extra=True)

where:

valid_tuple is a dataset identifier, like ("ecoinvent", "super strong steel")
uncertainty_fields are fields from an uncertainty dictionary.

Processing a Database actually produces two parameter arrays: one for the exchanges, which make up the technosphere and biosphere matrices, and a geomapping array which links activities to locations.

Parameters: *name* (unicode string) – Name of the database to manage.

property _metadata#

property filename#: Remove filesystem-unsafe characters and perform unicode normalization on self.name using filesystem.safe_filename().

property metadata#

property node_class#

property registered#

backend#

depends#

dirty#

extra#

filters#

geocollections#

name#

order_by#

searchable#

validator#

_add_indices()#

_drop_indices()#

_efficient_write_dataset(index, key, ds, exchanges, activities)#

_efficient_write_many_data(data, indices=True)#

_get_filters()#

_get_order_by()#

_get_queryset(random=False, filters=True)#

_iotable_edges_to_dataframe() → pandas.DataFrame#

Return a pandas DataFrame with all database exchanges. DataFrame columns are:

target_id: int, target_database: str, target_code: str, target_name: Optional[str], target_reference_product: Optional[str], target_location: Optional[str], target_unit: Optional[str], target_type: Optional[str] source_id: int, source_database: str, source_code: str, source_name: Optional[str], source_product: Optional[str], # Note different label source_location: Optional[str], source_unit: Optional[str], source_categories: Optional[str] # Tuple concatenated with “::” as in bw2io edge_amount: float, edge_type: str,

Target is the node consuming the edge, source is the node or flow being consumed. The terms target and source were chosen because they also work well for biosphere edges.

As IO Tables are normally quite large, the DataFrame building will operate directly on Numpy arrays, and therefore special formatters are not supported in this function.

Returns a pandas DataFrame.

_set_filters(filters)#

_set_order_by(field)#

_sqlite_edges_to_dataframe(categorical: bool = True, formatters: Optional[List[Callable]] = None) → pandas.DataFrame#

add_geomappings(data)#

backup()#

Save a backup to backups folder.

Returns: File path of backup.

classmethod clean_all()#

copy(name)#

Make a copy of the database.

Internal links within the database will be updated to match the new database name, i.e. ("old name", "some id") will be converted to ("new name", "some id") for all exchanges.

Parameters: name (*) – Name of the new database. Must not already exist.

datapackage()#

delete_data(keep_params=False, warn=True)#: Delete all data from SQLite database and Whoosh index

delete_duplicate_exchanges(fields=['amount', 'type'])#

Delete exchanges which are exact duplicates. Useful if you accidentally ran your input data notebook twice.

To determine uniqueness, we look at the exchange input and output nodes, and at the exchanges values for fields fields.

delete_instance()#

deregister()#: Legacy method to remove an object from the metadata store. Does not delete any data.

dirpath_processed()#

edges_to_dataframe(categorical: bool = True, formatters: Optional[List[Callable]] = None) → pandas.DataFrame#

Return a pandas DataFrame with all database exchanges. Standard DataFrame columns are:

target_id: int, target_database: str, target_code: str, target_name: Optional[str], target_reference_product: Optional[str], target_location: Optional[str], target_unit: Optional[str], target_type: Optional[str] source_id: int, source_database: str, source_code: str, source_name: Optional[str], source_product: Optional[str], # Note different label source_location: Optional[str], source_unit: Optional[str], source_categories: Optional[str] # Tuple concatenated with “::” as in bw2io edge_amount: float, edge_type: str,

Target is the node consuming the edge, source is the node or flow being consumed. The terms target and source were chosen because they also work well for biosphere edges.

Args:

categorical will turn each string column in a pandas Categorical Series. This takes 1-2 extra seconds, but saves around 50% of the memory consumption.

formatters is a list of callables that modify each row. These functions must take the following keyword arguments, and use the Wurst internal data format:

node: The target node, as a dict

edge: The edge, including attributes of the source node

row: The current row dict being modified.

The functions in formatters don’t need to return anything, they modify row in place.

Returns a pandas DataFrame.

exchange_data_iterator(sql, dependents, flip=False)#

Iterate over exchanges and format for bw_processing arrays.

dependents is a set of dependent database names.

flip means flip the numeric sign; see bw_processing docs.

Uses raw sqlite3 to retrieve data for ~2x speed boost.

classmethod exists(name)#

filename_processed()#

filepath_intermediate()#

filepath_processed(clean=True)#

find_dependents(data=None, ignore=None)#

Get sorted list of direct dependent databases (databases linked from exchanges).

Parameters

data (*) – Inventory data
ignore (*) – List of database names to ignore

Returns

List of database names

find_graph_dependents()#

Recursively get list of all dependent databases.

Returns: A set of database names

get_node(code=None, **kwargs)#

graph_technosphere(filename=None, **kwargs)#

load(*args, **kwargs)#

make_searchable(reset=False)#

make_unsearchable()#

new_activity(code, **kwargs)#

new_node(code=None, **kwargs)#

nodes_to_dataframe(columns: Optional[List[str]] = None, return_sorted: bool = True) → pandas.DataFrame#

Return a pandas DataFrame with all database nodes. Uses the provided node attributes by default, such as name, unit, location.

By default, returns a DataFrame sorted by name, reference product, location, and unit. Set return_sorted to False to skip sorting.

Specify columns to get custom columns. You will need to write your own function to get more customization, there are endless possibilities here.

Returns a pandas DataFrame.

process(csv=False)#

Create structured arrays for the technosphere and biosphere matrices.

Uses bw_processing for array creation and metadata serialization.

Also creates a geomapping array, linking activities to locations. Used for regionalized calculations.

Use a raw SQLite3 cursor instead of Peewee for a ~2 times speed advantage.

query(*queries)#: Search through the database.

random(filters=True, true_random=False)#: True random requires loading and sorting data in SQLite, and can be resource-intensive.

Legacy method to register a database with the metadata store. Writing data automatically sets the following metadata:

depends: Names of the databases that this database references, e.g. “biosphere”

number: Number of processes in this database.

relabel_data(data, new_name)#

Relabel database keys and exchanges.

In a database which internally refer to the same database, update to new database name new_name.

Needed to copy a database completely or cut out a section of a database.

For example:

data = {
    ("old and boring", 1):
        {"exchanges": [
            {"input": ("old and boring", 42),
            "amount": 1.0},
            ]
        },
    ("old and boring", 2):
        {"exchanges": [
            {"input": ("old and boring", 1),
            "amount": 4.0}
            ]
        }
    }
print(relabel_database(data, "shiny new"))
>> {
    ("shiny new", 1):
        {"exchanges": [
            {"input": ("old and boring", 42),
            "amount": 1.0},
            ]
        },
    ("shiny new", 2):
        {"exchanges": [
            {"input": ("shiny new", 1),
            "amount": 4.0}
            ]
        }
    }

In the example, the exchange to ("old and boring", 42) does not change, as this is not part of the updated data.

Parameters

data (*) – The data to modify
new_name (*) – The name of the modified database

Returns

The modified data

rename(name)#

Rename a database. Modifies exchanges to link to new name.

Parameters: name (*) – New name.
Returns: self # Backwards compatibility

search(string, **kwargs)#

Search this database for string.

The searcher include the following fields:

name
comment
categories
location
reference product

string can include wild cards, e.g. "trans*".

By default, the name field is given the most weight. The full weighting set is called the boost dictionary, and the default weights are:

{
    "name": 5,
    "comment": 1,
    "product": 3,
    "categories": 2,
    "location": 3
}

Optional keyword arguments:

limit: Number of results to return.
boosts: Dictionary of field names and numeric boosts - see default boost values above. New values must be in the same format, but with different weights.
filter: Dictionary of criteria that search results must meet, e.g. {'categories': 'air'}. Keys must be one of the above fields.
mask: Dictionary of criteria that exclude search results. Same format as filter.
facet: Field to facet results. Must be one of name, product, categories, location, or database.
proxy: Return Activity proxies instead of raw Whoosh documents. Default is True.

Returns a list of Activity datasets.

classmethod set_dirty(name)#

set_geocollections()#: Set geocollections attribute for databases which don’t currently have it.

validate(data)#

write(data, process=True)#

Write data to database.

data must be a dictionary of the form:

{
    ('database name', 'dataset code'): {dataset}
}

Writing a database will first deletes all existing data.

write_exchanges(technosphere, biosphere, dependents)#

Write IO data directly to processed arrays.

Product data is stored in SQLite as normal activities. Exchange data is written directly to NumPy structured arrays.

Technosphere and biosphere data has format (row id, col id, value, flip).

class bw2data.backends.Exchange(document=None, **kwargs)[source]#

Bases: bw2data.proxies.ExchangeProxyBase

Inheritance diagram of bw2data.backends.Exchange

A MutableMapping is a generic container for associating key/value pairs.

This class provides concrete generic implementations of all methods except for __getitem__, __setitem__, __delitem__, __iter__, and __len__.

Create an Exchange proxy object.

If this is a new exchange, can pass kwargs.

If the exchange exists in the database, document should be an ExchangeDataset.

delete()#

save()#

class bw2data.backends.ExchangeDataset[source]#

Bases: peewee.Model

Inheritance diagram of bw2data.backends.ExchangeDataset

data#

input_code#

input_database#

output_code#

output_database#

type#

class bw2data.backends.SubstitutableDatabase(filepath, tables)[source]#

property db#

_create_database()#

atomic()#

change_path(filepath)#

execute_sql(*args, **kwargs)#

transaction()#

vacuum()#

bw2data.backends.get_id(key)[source]#

bw2data.backends.Edge[source]#

bw2data.backends.Node[source]#

bw2data.backends.SQLiteBackend[source]#

bw2data.backends.projects[source]#

bw2data.backends.sqlite3_lci_db[source]#

bw2data.backends#

Submodules#

Package Contents#

Classes#

Functions#

Attributes#

`bw2data.backends`#