pyvolca 0.4.0

Python client for VoLCA — Life Cycle Assessment engine

pyvolca

Python client for VoLCA — Life Cycle Assessment engine over Agribalyse and ecoinvent.

Full guide and tutorials: https://volca.run/docs/python/ Issues / source: https://github.com/ccomb/volca

Install

pip install pyvolca

Requires Python ≥ 3.10 and a running VoLCA engine. Use Server (below) to run one as a child process, or point Client at any reachable instance.

First choose: connect to an existing server, or start one locally

pyvolca is only the Python client library. It does not contain the VoLCA databases and it does not install the VoLCA engine binary.

Most users should start with one of these two modes:

• You already have access to a VoLCA server (for example a hosted server prepared by someone else): use Client only. You do not need volca.toml, and you do not need to install the VoLCA server locally.
• You want Python to start a local VoLCA engine process for you: use download() once to fetch the VoLCA engine binary and reference data into the shared volca install dir (see Where artefacts are installed), then use Server to start it from Python. volca.toml is still a normal file path passed to Server(config=...); put it in your project directory, or pass an absolute path. Do not put it inside your virtualenv or inside site-packages.

For a hosted server, the minimal connection looks like this:

# no-test  — replace with your real hosted VoLCA server URL and credentials.
from volca import Client

c = Client(
    base_url="https://your-volca-server.example.com",
    db="agribalyse-3.2",
    password="your-api-token-or-password",
)

print(c.list_databases())

Use download() + Server only when you deliberately want to download and launch the engine from Python:

# no-test  — downloads the engine and needs a real engine config/database.
from volca import Client, Server, download

installed = download()  # cached after the first run

with Server(config="./volca.toml", binary=str(installed.binary)) as srv:
    c = Client(base_url=srv.base_url, db="agribalyse-3.2", password=srv.password)
    print(c.list_databases())

In this local mode, download() stores the engine binary and reference data in the shared volca install dir (see below). Server(config="./volca.toml") still means “read ./volca.toml relative to the current working directory”.

Where artefacts are installed

download() writes to the same OS-native location as the install.sh / install.ps1 shell installers, so any of the three tools populate the same directory:

Platform	Default install root
Linux	${XDG_DATA_HOME:-~/.local/share}/volca/
macOS	~/Library/Application Support/volca/
Windows	%LOCALAPPDATA%\volca\

Override with VOLCA_HOME=/full/path (full path; skips OS detection).

If you ran install.sh or install.ps1 first, Server() finds the installed engine without an extra download() call. If you previously used pyvolca < 0.4 it cached artefacts under <user_cache_dir>/pyvolca/ (Linux: ~/.cache/pyvolca/); that directory is no longer read and can be removed (rm -rf ~/.cache/pyvolca).

Local managed-server quick start

# no-test  — needs a real engine; the snippets below run against a mocked Client.
from volca import Client, Server

with Server(config="volca.toml") as srv:
    c = Client(base_url=srv.base_url, db="agribalyse-3.2", password=srv.password)
    plants = c.search_activities(name="wheat flour, at plant", limit=5)
    chain = c.get_supply_chain(plants[0].process_id, name="at farm")
    score = c.get_impacts(plants[0].process_id, method_id=c.list_methods()[0]["id"])

This example starts a local engine process from Python. Server reads port and password from the TOML config. The engine self-stops after idle_timeout seconds without traffic (default 5 min).

Examples below assume c is a Client instance — construct it with the snippet above, or against an already-running server: c = Client(base_url="http://localhost:8080", db="agribalyse-3.2", password="…").

Discover what's available

Which databases are loaded? Which LCIA methods can I score against? What classification systems can I filter on?

for db in c.list_databases():
    print(f"  {db.name} [{db.status}]: {db.activity_count} activities")

for m in c.list_methods()[:5]:
    print(f"  {m['id']}  {m['name']} [{m['unit']}]")

Other listings: c.list_classifications() returns the classification systems and their values for the current database; c.list_presets() returns named filter presets configured in the engine. Use c.load_database(name) / c.unload_database(name) to manage memory if a database isn't auto-loaded.

Find an activity

Which activity in the database represents the product I want to assess?

plants = c.search_activities(name="wheat flour, at plant", limit=5)
for a in plants:
    print(f"{a.process_id}  {a.name} ({a.location})")

Each Activity carries process_id, name, location, product, product_amount, product_unit. Narrow the query with geo="FR", classification=/classification_value= (ISIC/CPC), or set exact=True for an exact-name match. To search by flow name (technosphere products and biosphere flows) instead of activity name, use c.search_flows(query=...).

Inspect an activity

What goes into making this product? What does it emit? What's its reference unit?

detail = c.get_activity(plants[0].process_id)
for ex in detail.technosphere_inputs:
    print(f"{ex.amount:.4g} {ex.unit} of {ex.flow_name} ← {ex.target_activity}")

get_activity returns a typed ActivityDetail. Use .inputs / .outputs / .technosphere_inputs to filter the exchanges; each entry is an Exchange — either a TechnosphereExchange (an input or output of an intermediate product) or a BiosphereExchange (resource extracted or pollutant emitted).

Trace the upstream supply chain

What's the full upstream chain — every ingredient, recursively, down to the farm or mine?

chain = c.get_supply_chain(plants[0].process_id, name="at farm", limit=20)
print(f"{chain.filtered_activities} of {chain.total_activities} upstream activities match 'at farm'")
for entry in chain.entries[:5]:
    print(f"  {entry.quantity:.4g} {entry.unit} of {entry.name} ({entry.location})")

For "how exactly does this root reach a specific upstream supplier?", use get_path_to(process_id, target=...) — returns a PathResult of ordered PathSteps root → target with cumulative quantities and step ratios.

Find downstream consumers

Where is this supplier used? Which products depend on it?

result = c.get_consumers(plants[0].process_id, max_depth=2, limit=10)
for cons in result.consumers:
    print(f"  depth={cons.depth}  {cons.name} ({cons.location})")

Returns a ConsumersResponse with consumers, pagination, and (when include_edges=True) the technosphere edges so callers can reconstruct supplier→consumer paths without a second round trip. Pass classification_filters=[...] to restrict to a category.

Compute the life-cycle inventory

What are the cumulative biosphere flows (CO₂, water, methane, …) per functional unit, before applying any characterization method?

inv = c.get_inventory(plants[0].process_id, limit=20)
# inv is a raw dict — see the OpenAPI spec for the full shape.
# Substitutions are accepted: c.get_inventory(pid, substitutions=[...])

The inventory is what every LCIA method runs on top of. If you only need grouped views (by name, location, classification, etc.), reach for c.aggregate(scope="biosphere", group_by=...) instead — same data, summarized.

Compute environmental impacts (LCIA)

What's the carbon footprint of this product? Which emissions dominate the score?

score = c.get_impacts(plants[0].process_id, method_id="EF3.1-climate-change", top_flows=5)
print(f"{score.score:.4g} {score.unit}")
for c_flow in score.top_contributors:
    print(f"  {c_flow.share_pct:.1f}%  {c_flow.flow_name}")

LCIAResult carries the score, unit, optional normalized_score / weighted_score (in Pt), and the top contributing biosphere flows with their share_pct.

Compute every impact category in one go — climate, water, land use, …

batch = c.get_impacts_batch(plants[0].process_id)
for r in batch.results:
    print(f"  {r.category}: {r.score:.4g} {r.unit}")
if batch.single_score is not None:
    print(f"PEF single score: {batch.single_score:.4g} {batch.single_score_unit}")

LCIABatchResult also surfaces formula-based scoring sets (PEF, ECS…) via scoring_results and scoring_indicators, so you can render a per-indicator chart alongside the aggregate single score.

Drill into what drives a single impact

I have a climate-change score. Which biosphere flows account for it? Which upstream activities?

get_impacts(...).top_contributors already returns the top biosphere flows for a single LCIA call. For a deeper or differently-bounded view — and for the activity attribution view — use the standalone drill-down endpoints:

flows = c.get_contributing_flows(
    plants[0].process_id,
    method_id="EF3.1-climate-change",
    limit=10,
)
acts = c.get_contributing_activities(
    plants[0].process_id,
    method_id="EF3.1-climate-change",
    limit=10,
)
# Both return raw dicts — the shape is documented in the OpenAPI spec.

Which characterization factors does a method apply, and to which database flows?

char = c.get_characterization(method_id="EF3.1-climate-change", limit=20)

Useful for sanity-checking method coverage or building custom indicators on top of the engine's mapping.

Aggregate flows by group

What are the top emitting substances? How do flows break down by category, location, or classification?

agg = c.aggregate(
    plants[0].process_id,
    scope="biosphere",
    group_by="name",
    aggregate="sum_quantity",
)
for g in agg.groups[:5]:
    print(f"  {g.quantity:.4g} {g.unit or ''} of {g.key}")

scope selects what to aggregate over: "direct" (just this activity's exchanges), "supply_chain" (cumulative upstream), or "biosphere" (all elementary flows). group_by accepts "name", "flow_id", "unit", "location", "target_name", or "classification.<system>".

Compare two activities

How does variant A differ from variant B? Which inputs change?

from volca import compare_activities

diff = compare_activities(c, plants[0].process_id, plants[1].process_id, scope="direct")
print(f"  matched: {len(diff.matched)}, only-left: {len(diff.left_only)}, only-right: {len(diff.right_only)}")
for row in diff.matched[:3]:
    print(f"    {row.key}: {row.left:.4g} → {row.right:.4g}  (Δ={row.delta:+.4g})")

A client-side merge over two aggregate calls. Groups by flow_id (default) so matching is stable across naming variants. Pass scope="supply_chain" to compare cumulative inputs instead of direct exchanges.

Run counterfactuals (substitutions)

What if I used organic wheat instead of conventional? Recycled aluminium instead of virgin? — without reloading the database.

The engine applies a Sherman–Morrison rank-1 update, so substitutions are fast regardless of database size. Works on get_supply_chain, get_inventory, and get_impacts.

subs = [{
    "from": "old-supplier-pid",      # the activity to replace
    "to":   "new-supplier-pid",      # the replacement
    "consumer": "consumer-pid",      # the activity that directly uses the old supplier
}]
score = c.get_impacts(plants[0].process_id, method_id="EF3.1-climate-change", substitutions=subs)

Multiple substitutions chain in one call — the consumer field disambiguates where in the chain each swap applies.

Handle errors

The activity doesn't exist, the engine is down, or the request is malformed — what do I catch?

from volca import VoLCAError

try:
    score = c.get_impacts("nonexistent-pid", method_id="EF3.1-climate-change")
except VoLCAError as e:
    print(f"  failed: {e.status_code} — {e.body[:80]}")

VoLCAError.status_code is the HTTP status when the engine returned one; body is the raw response body.

Switch databases

I want to run the same workflow against ecoinvent instead of Agribalyse — without rebuilding the client.

ei = c.use("ecoinvent-3.10")
ei_results = ei.search_activities(name="electricity, high voltage")

Client.use(db_name) returns a new Client targeting a different database while sharing the HTTP session and dispatch table — no spec re-fetch.

Refresh IDE autocomplete after upgrading the engine

I just upgraded the VoLCA server. How do I get my editor to see the new endpoints?

c.refresh_stubs()

Pyvolca dispatches dynamically against the engine's OpenAPI spec, so it ships without .pyi stubs. refresh_stubs() refetches the spec and writes stubs into the installed package directory; restart your language server to pick them up.

API reference

This reference is generated from the installed package. Run python scripts/gen_api_md.py to regenerate.

Classes

Client

HTTP client for the VoLCA HTTP API.

Usage::

c = Client(db="agribalyse-3.2", password="1234")
plants = c.search_activities(name="at plant")
chain = c.get_supply_chain(plants[0].process_id, name="at farm")

Substitutions can be passed to get_supply_chain, get_inventory, and get_impacts to compute results with a different upstream supplier — fast::

subs = [{"from": old_pid, "to": new_pid, "consumer": consumer_pid}]
result = c.get_impacts(pid, method_id=mid, substitutions=subs)

Constructor: Client(base_url: str = 'http://localhost:8080', db: str = '', password: str = '')

Methods:

• aggregate(process_id: str, scope: str, *, is_input: bool | None = None, max_depth: int | None = None, filter_name: str | None = None, filter_name_not: list[str] | str | None = None, filter_unit: str | None = None, preset: str | None = None, filter_classification: list[ClassificationFilter] | None = None, filter_target_name: str | None = None, filter_is_reference: bool | None = None, group_by: str | None = None, aggregate: str | None = None) -> AggregateResult — SQL-group-by aggregation over direct exchanges, supply chain, or biosphere flows.
• call(operation_id: str, **kwargs) -> Any — Escape hatch: call any OpenAPI operation by operationId.
• get_activity(process_id: str) -> ActivityDetail — Fetch an activity's full detail.
• get_characterization(method_id: str, *, flow: str | None = None, limit: int | None = None) -> dict — Look up characterization factors for a method matched to database flows.
• get_consumers(process_id: str, *, name: str | None = None, location: str | None = None, product: str | None = None, preset: str | None = None, classification_filters: list[ClassificationFilter] | None = None, limit: int | None = None, max_depth: int | None = None, include_edges: bool = False) -> ConsumersResponse — Find all activities that transitively consume this supplier.
• get_contributing_activities(process_id: str, method_id: str, *, collection: str = 'methods', limit: int | None = None) -> dict — Which upstream activities drive a given impact category.
• get_contributing_flows(process_id: str, method_id: str, *, collection: str = 'methods', limit: int | None = None) -> dict — Which elementary flows drive a given impact category.
• get_flow_mapping(method_id: str) -> dict — Get the characterization-factor-to-database-flow mapping coverage.
• get_impacts(process_id: str, method_id: str, *, collection: str = 'methods', top_flows: int | None = None, substitutions: list[dict] | None = None) -> LCIAResult — Compute the LCIA score for a single impact category on an activity.
• get_impacts_batch(process_id: str, *, collection: str = 'methods', substitutions: list[dict] | None = None) -> LCIABatchResult — Compute LCIA for every impact category in a collection, in one call.
• get_inputs(process_id: str) -> list[Exchange] — Return the input exchanges of an activity (richer metadata than get_activity).
• get_inventory(process_id: str, *, flow: str | None = None, limit: int | None = None, substitutions: list[dict] | None = None) -> dict — Compute the life-cycle inventory (cumulative biosphere flows) for an activity.
• get_outputs(process_id: str) -> list[Exchange] — Return the output exchanges of an activity. See :meth:get_inputs for notes.
• get_path_to(process_id: str, target: str) -> PathResult — Find the shortest upstream path from process to first activity whose name matches target.
• get_supply_chain(process_id: str, *, name: str | None = None, location: str | None = None, limit: int | None = None, min_quantity: float | None = None, max_depth: int | None = None, preset: str | None = None, classification_filters: list[ClassificationFilter] | None = None, substitutions: list[dict] | None = None, include_edges: bool | None = None) -> SupplyChain — Get the flat supply chain of an activity.
• get_tree(process_id: str) -> dict — Fetch the recursive activity tree used by the analysis SPA.
• get_version() — Return server version info (version, gitHash, gitTag, buildTarget).
• list_classifications() — List classification systems and their values for the current database.
• list_databases() — List every database declared in the engine config.
• list_methods() — List every LCIA method available in the engine.
• list_presets() — List classification presets configured in this instance.
• load_database(db_name: str) -> dict — Load a database into memory so it answers queries.
• refresh_stubs() — Fetch the OpenAPI spec from the server and refresh the dispatch table.
• search_activities(name: str | None = None, *, geo: str | None = None, product: str | None = None, preset: str | None = None, classification: str | None = None, classification_value: str | None = None, limit: int | None = None, offset: int = 0, exact: bool = False) -> list[Activity] — Search activities in the current database.
• search_flows(query: str | None = None, *, limit: int | None = None) -> list[dict] — Search flows (technosphere products and biosphere flows) in the current database.
• unload_database(db_name: str) -> dict — Unload a database from memory to free RAM. The disk copy is kept.
• use(db_name: str) -> 'Client' — Return a new client targeting a different database (shares session).

Server

Manages the VoLCA server process.

Usage::

with Server(config="volca.toml") as srv:
    client = Client(base_url=srv.base_url, db="agribalyse-3.2", password=srv.password)
    activities = client.search_activities(name="at plant")

Constructor: Server(config: str = 'volca.toml', port: int = 0, binary: str = 'volca')

Properties:

• base_url — (no docstring)

Methods:

• is_alive() — Health check — GET /api/v1/db, return True if 200.
• start(idle_timeout: int = 300, wait_timeout: int = 120) -> None — Spawn the engine process if it is not already serving, and wait until ready.
• stop() — Stop the server via shutdown endpoint, then terminate process.

Exceptions

DownloadError

Raised when the download or verification fails.

VoLCAError

Error from the VoLCA API.

Constructor: VoLCAError(message: str, status_code: int | None = None, body: str = '')

Data types

Activity

Field	Type	Default
process_id	str	—
name	str	—
location	str	—
product	str	—
product_amount	float	—
product_unit	str	—

ActivityDetail

Typed wrapper around the JSON returned by GET /activity/{pid}.

Use the .inputs / .outputs / .technosphere_inputs convenience properties instead of walking the raw exchanges list.

Field	Type	Default
process_id	str	—
name	str	—
location	str	—
unit	str	—
description	list[str]	—
classifications	dict[str, str]	—
reference_product	str | None	—
reference_product_amount	float | None	—
reference_product_unit	str | None	—
all_products	list[Activity]	—
exchanges	list[Union[TechnosphereExchange, BiosphereExchange]]	—

ActivityDiff

Result of compare_activities.

Field	Type	Default
scope	str	—
group_by	str	—
matched	list[ActivityDiffRow]	list()
left_only	list[ActivityDiffRow]	list()
right_only	list[ActivityDiffRow]	list()

ActivityDiffRow

One matched or unmatched flow in an activity comparison.

Field	Type	Default
key	str	—
left	float | None	—
right	float | None	—
unit	str | None	—

AggregateGroup

One bucket inside an AggregateResult.

Field	Type	Default
key	str	—
quantity	float	—
count	int	—
unit	str | None	None
share	float | None	None

AggregateResult

Result of a Client.aggregate() call.

filtered_total is the sum across all items matching the filters (the top-level number). groups is the per-bucket breakdown when group_by was set; empty otherwise.

Field	Type	Default
scope	str	—
filtered_total	float	—
filtered_unit	str | None	—
filtered_count	int	—
groups	list[AggregateGroup]	list()

BiosphereExchange

An exchange with the environment (resource extraction or emission).

Field	Type	Default
flow_name	str	—
compartment	Compartment | None	—
amount	float	—
unit	str	—
direction	Literal['Resource', 'Emission']	—
comment	str | None	None
is_biosphere	bool	True

ClassificationFilter

Filter a supply-chain/consumers query by a classification (system, value, mode).

Matches one classification system entry (e.g. ("Category", "Agricultural\Food", "exact")). Mode is "exact" (case-insensitive equality) or "contains" (substring). Multiple filters are AND-combined by the server.

Field	Type	Default
system	str	—
value	str	—
mode	str	'contains'

Compartment

Biosphere compartment (medium + optional subcompartment).

Frozen so it's hashable and immutable — callers can use it as a dict key when grouping flows by compartment, and accidental mutation is rejected.

Field	Type	Default
name	str	—
sub	str | None	None

ConsumerResult

Activity that consumes a given supplier, with BFS depth.

Field	Type	Default
process_id	str	—
name	str	—
location	str	—
product	str	—
product_amount	float	—
product_unit	str	—
depth	int	—
classifications	dict[str, str]	dict()

ConsumersResponse

Reverse supply chain (/consumers) — paginated consumer list plus optional edge set. Mirrors :class:SupplyChain so callers have a uniform {entries, edges} shape in both traversal directions. edges is populated only when include_edges=True.

Field	Type	Default
consumers	list[ConsumerResult]	—
total	int	—
offset	int	—
limit	int	—
has_more	bool	—
search_time_ms	float	—
edges	list[SupplyChainEdge]	list()

DatabaseInfo

One entry of :meth:Client.list_databases.

depends_on names the databases this one links against for cross-DB flow resolution — mirrors the dependsOn list surfaced by the relink endpoint. Derived from the engine's declared topology, not runtime state.

Field	Type	Default
name	str	—
display_name	str	—
status	str	—
path	str	—
load_at_startup	bool	False
is_uploaded	bool	False
activity_count	int	0
description	str | None	None
format	str | None	None
depends_on	list[str]	list()

FlowContribution

Top contributing elementary flow for an impact category.

Emitted inside LCIAResult.top_contributors.

Field	Type	Default
flow_name	str	—
contribution	float	—
share_pct	float	—
flow_id	str	—
category	str	—
cf_value	float	0.0
compartment	str | None	None

Installed

Result of :func:download.

Field	Type	Default
binary	Path	—
data_dir	Path	—
version	str	—
data_version	str	—

LCIABatchResult

Batch LCIA: every impact category in a method collection, for one activity.

Returned by :meth:Client.get_impacts_batch. Carries the per-method impact results plus any formula-based scoring sets configured in the engine TOML (PEF, ECS, or any named set).

scoring_indicators gives the per-variable normalized-weighted breakdown of each scoring set — already multiplied by the set's displayMultiplier and expressed in its display unit (see :class:ScoringIndicator). Lets callers render per-indicator charts alongside the aggregate scoring_results.

Field	Type	Default
results	list[LCIAResult]	—
single_score	float | None	None
single_score_unit	str | None	None
norm_weight_set_name	str | None	None
available_nw_sets	list[str]	list()
scoring_results	dict[str, dict[str, float]]	dict()
scoring_units	dict[str, str]	dict()
scoring_indicators	dict[str, dict[str, ScoringIndicator]]	dict()

LCIAResult

LCIA score for one impact category on one activity.

Returned directly by :meth:Client.get_impacts, and nested inside :class:LCIABatchResult.results (one entry per impact category).

Field	Type	Default
method_id	str	—
method_name	str	—
category	str	—
damage_category	str	—
score	float	—
unit	str	—
mapped_flows	int	—
functional_unit	str	—
normalized_score	float | None	None
weighted_score	float | None	None
top_contributors	list[FlowContribution]	list()

PathResult

Shortest upstream path from a root process to a matching activity.

Field	Type	Default
path	list[PathStep]	—
path_length	int	—
total_ratio	float	—

PathStep

One step in the supply chain path returned by get_path_to.

Note: the /path endpoint emits snake_case JSON directly (built via aeson's object [...] rather than generic ToJSON), so it bypasses the engine's stripLowerPrefix transform.

Field	Type	Default
process_id	str	—
name	str	—
location	str	—
unit	str	—
cumulative_quantity	float	—
scaling_factor	float	—
local_step_ratio	float | None	None

ScoringIndicator

One per-variable entry inside LCIABatchResult.scoring_indicators.

value is pre-multiplied by the scoring set's displayMultiplier (configured in the scoring TOML) and expressed in the set's display unit. category names the impact category the variable was resolved from.

Field	Type	Default
category	str	—
value	float	—

SupplyChain

Field	Type	Default
root	Activity	—
total_activities	int	—
filtered_activities	int	—
entries	list[SupplyChainEntry]	list()
edges	list[SupplyChainEdge]	list()

SupplyChainEdge

from/to are Python keywords, so they're stored under from_id/to_id.

Field	Type	Default
from_id	str	—
to_id	str	—
amount	float	—

SupplyChainEntry

Field	Type	Default
process_id	str	—
name	str	—
location	str	—
quantity	float	—
unit	str	—
scaling_factor	float	—
classifications	dict[str, str]	dict()

TechnosphereExchange

An exchange with another activity. Carries no compartment — the producing activity's classifications describe the product taxonomy.

Field	Type	Default
flow_name	str	—
amount	float	—
unit	str	—
role	Literal['ReferenceProduct', 'Coproduct', 'ReferenceInput', 'Input']	—
target_activity	str | None	—
target_location	str | None	—
target_process_id	str | None	—
comment	str | None	None
is_biosphere	bool	False

Functions

compare_activities(client: Client, pid_left: str, pid_right: str, *, scope: str = 'direct', group_by: str = 'flow_id', is_input: bool | None = True, **aggregate_kwargs) -> ActivityDiff

Diff two activities by flow_id (default) at the requested scope.

Returns three lists:

• matched: flows present in both activities (with left, right, delta).
• left_only: flows present only in the left activity.
• right_only: flows present only in the right activity.

Default is_input=True restricts the comparison to inputs, which is the common case for "what does this variant consume differently?". Pass is_input=None to include outputs as well.

download(version: Optional[str] = None, repo: str = 'ccomb/volca', *, force: bool = False) -> Installed

Download the volca binary + data bundle for the current platform.

Idempotent: if both artefacts are already extracted under the install root and force=False, returns immediately without network.

Args: version: GH Release tag (v0.7.0); None resolves the latest. repo: GitHub repo slug. Default ccomb/volca. force: Re-download even if the install root looks complete.

Returns: :class:Installed with the resolved paths and versions.

Type aliases

Exchange

Type alias: Union[TechnosphereExchange, BiosphereExchange].

TechRole

Type alias: Literal['ReferenceProduct', 'Coproduct', 'ReferenceInput', 'Input'].

See also

• Full guide and tutorials: https://volca.run/docs/python/
• VoLCA engine: https://github.com/ccomb/volca
• Examples folder: examples/

License

Apache-2.0