API reference#
Top-level classes and functions#
|
Configure |
|
Class for describing and executing computations. |
|
A hashable key for a quantity that includes its dimensionality. |
|
A sparse data structure that behaves like |
- genno.configure(path: Path | str | None = None, **config)[source]
Configure
genno
globally.Modifies global variables that affect the behaviour of all Computers and computations. Configuration keys loaded from file are superseded by keyword arguments. Messages are logged at level
logging.INFO
if config contains unhandled sections.- Parameters:
path (Path, optional) – Path to a configuration file in JSON or YAML format.
**config – Configuration keys/sections and values.
- class genno.Computer(**kwargs)[source]#
Class for describing and executing computations.
- Parameters:
kwargs – Passed to
configure()
.
A Computer is used to describe (
add()
and related methods) and then execute (get()
and related methods) tasks stored in agraph
. Advanced users may manipulate the graph directly; but common reporting tasks can be handled by using Computer methods.Instance attributes:
The default key to
get()
with no argument.keys
()Return the keys of
graph
.List of modules containing pre-defined computations.
The
pint.UnitRegistry()
used by the Computer.General-purpose methods for describing tasks and preparing computations:
add
(data, *args, **kwargs)General-purpose method to add computations.
add_queue
(queue[, max_tries, fail])Add tasks from a list or queue.
add_single
(key, *computation[, strict, index])Add a single computation at key.
apply
(generator, *keys, **kwargs)Add computations by applying generator to keys.
cache
(func)Decorate func so that its return value is cached.
describe
([key, quiet])Return a string describing the computations that produce key.
visualize
(filename[, key, optimize_graph])Generate an image describing the Computer structure.
Helper methods to simplify adding specific computations:
add_file
(path[, key])Add exogenous quantities from path.
add_product
(key, *quantities[, sums])Add a computation that takes the product of quantities.
aggregate
(qty, tag, dims_or_groups[, ...])Add a computation that aggregates qty.
convert_pyam
(quantities[, tag])Add conversion of one or more quantities to IAMC format.
disaggregate
(qty, new_dim[, method, args])Add a computation that disaggregates qty using method.
Exectuing tasks:
get
([key])Execute and return the result of the computation key.
write
(key, path)Write the result of key to the file path.
Utility and configuration methods:
check_keys
(*keys[, action])Check that keys are in the Computer.
configure
([path, fail, config])Configure the Computer.
full_key
(name_or_key)Return the full-dimensionality key for name_or_key.
get_comp
(name)Return a computation function.
infer_keys
(key_or_keys[, dims])Infer complete key_or_keys.
require_compat
(pkg)Register computations from
genno.compat
/others forget_comp()
.- graph: Graph = {'config': {}}#
A dask-format graph (see 1, 2).
Dictionary keys are either
Key
,str
, or any other hashable value.Dictionary values are computations, one of:
Any other, existing key in the Computer. This functions as an alias.
Any other literal value or constant, to be returned directly.
A task
tuple
: a callable (e.g. function), followed by zero or more computations, e.g. keys for other tasks.A
list
containing zero or more of (1), (2), and/or (3).
genno
reserves some keys for special usage:"config"
A
dict
storing configuration settings. See Configuration. Because this information is stored in thegraph
, it can be used as one input to other computations.
Some inputs to tasks may be confused for (1) or (4), above. The recommended way to protect these is:
Literal
str
inputs to tasks: usefunctools.partial()
on the function that is the first element of the task tuple.
- add(data, *args, **kwargs)[source]#
General-purpose method to add computations.
add()
can be called in several ways; its behaviour depends on data; see below. It chains to methods such asadd_single()
,add_queue()
, and/orapply()
; each can also be called directly.- Returns:
Some or all of the keys added to the Computer.
- Return type:
list of Key-like
See also
The data argument may be:
list
A list of computations, like
[(list(args1), dict(kwargs1)), (list(args2), dict(kwargs2)), ...]
→ passed toadd_queue()
.str
naming a computatione.g. “select”, retrievable with
get_comp()
.add_single()
is called with(key=args[0], data, *args[1], **kwargs
, i.e. applying the named computation. to the other parameters.str
naming another Computer methode.g.
add_file()
→ the named method is called with the args and kwargs.Key
or otherstr
:Passed to
add_single()
.
add()
may be used to:Provide an alias from one key to another:
>>> from genno import Computer >>> rep = Computer() # Create a new Computer object >>> rep.add('aliased name', 'original name')
Define an arbitrarily complex computation in a Python function that operates directly on the
ixmp.Scenario
:>>> def my_report(scenario): >>> # many lines of code >>> return 'foo' >>> rep.add('my report', (my_report, 'scenario')) >>> rep.finalize(scenario) >>> rep.get('my report') foo
- apply(generator, *keys, **kwargs)[source]#
Add computations by applying generator to keys.
- Parameters:
generator (callable) – Function to apply to keys.
keys (hashable) – The starting key(s).
kwargs – Keyword arguments to generator.
The generator may have a type annotation for Computer on its first positional argument. In this case, a reference to the Computer is supplied, and generator can use the Computer methods to add many keys and computations:
def my_gen0(c: genno.Computer, **kwargs): c.load_file("file0.txt", **kwargs) c.load_file("file1.txt", **kwargs) # Use the generator to add several computations rep.apply(my_gen0, units="kg")
Or, generator may
yield
a sequence (0 or more) of (key, computation), which are added to thegraph
:def my_gen1(**kwargs): op = partial(computations.load_file, **kwargs) yield from (f"file:{i}", (op, "file{i}.txt")) for i in range(2) rep.apply(my_gen1, units="kg")
- convert_pyam(quantities, tag='iamc', **kwargs)[source]#
Add conversion of one or more quantities to IAMC format.
- Parameters:
- Returns:
Each task converts a
Quantity
into apyam.IamDataFrame
.- Return type:
See also
The IAMC data format includes columns named ‘Model’, ‘Scenario’, ‘Region’, ‘Variable’, ‘Unit’; one of ‘Year’ or ‘Time’; and ‘value’.
Using
convert_pyam()
:‘Model’ and ‘Scenario’ are populated from the attributes of the object returned by the Reporter key
scenario
;‘Variable’ contains the name(s) of the quantities;
‘Unit’ contains the units associated with the quantities; and
‘Year’ or ‘Time’ is created according to year_time_dim.
A callback function (collapse) can be supplied that modifies the data before it is converted to an
IamDataFrame
; for instance, to concatenate extra dimensions into the ‘Variable’ column. Other dimensions can simply be dropped (with drop). Dimensions that are not collapsed or dropped will appear as additional columns in the resultingIamDataFrame
; this is valid, but non-standard IAMC data.For example, here the values for the MESSAGEix
technology
andmode
dimensions are appended to the ‘Variable’ column:def m_t(df): """Callback for collapsing ACT columns.""" # .pop() removes the named column from the returned row df['variable'] = 'Activity|' + df['t'] + '|' + df['m'] return df ACT = rep.full_key('ACT') keys = rep.convert_pyam(ACT, 'ya', collapse=m_t, drop=['t', 'm'])
- add_aggregate(qty: Key | Hashable, tag: str, dims_or_groups: Mapping | str | Sequence[str], weights: DataArray | None = None, keep: bool = True, sums: bool = False, fail: str | int | None = None)#
Add a computation that aggregates qty.
- Parameters:
qty (
Key
or str) – Key of the quantity to be aggregated.tag (str) – Additional string to add to the end the key for the aggregated quantity.
dims_or_groups (str or iterable of str or dict) – Name(s) of the dimension(s) to sum over, or nested dict.
weights (
xarray.DataArray
, optional) – Weights for weighted aggregation.keep (bool, optional) – Passed to
computations.aggregate
.fail (str or int, optional) – Passed to
add_queue()
viaadd()
.
- Returns:
The key of the newly-added node.
- Return type:
- add_file(path, key=None, **kwargs)[source]#
Add exogenous quantities from path.
Computing the key or using it in other computations causes path to be loaded and converted to
Quantity
.- Parameters:
path (os.PathLike) – Path to the file, e.g. ‘/path/to/foo.ext’.
key (str or .Key, optional) – Key for the quantity read from the file.
dims (dict or list or set) – Either a collection of names for dimensions of the quantity, or a mapping from names appearing in the input to dimensions.
units (str or pint.Unit) – Units to apply to the loaded Quantity.
- Returns:
Either key (if given) or e.g.
file:foo.ext
based on the path name, without directory components.- Return type:
.Key
See also
- add_product(key, *quantities, sums=True)[source]#
Add a computation that takes the product of quantities.
- Parameters:
- Returns:
The full key of the new quantity.
- Return type:
- add_queue(queue: Iterable[Tuple[Tuple, Mapping]], max_tries: int = 1, fail: str | int | None = None) Tuple[Key | Hashable, ...] [source]#
Add tasks from a list or queue.
- Parameters:
queue (iterable of 2-
tuple
) – The members of each tuple are the arguments (e.g.list
or tuple) and keyword arguments (e.gdict
) toadd()
.max_tries (int, optional) – Retry adding elements up to this many times.
fail (“raise” or str or
logging
level, optional) – Action to take when a computation from queue cannot be added after max_tries: “raise” an exception, or log messages on the indicated level and continue.
- add_single(key, *computation, strict=False, index=False)[source]#
Add a single computation at key.
- Parameters:
key (str or Key or hashable) – A string, Key, or other value identifying the output of computation.
strict (bool, optional) – If True, key must not already exist in the Computer, and any keys referred to by computation must exist.
index (bool, optional) – If True, key is added to the index as a full-resolution key, so it can be later retrieved with
full_key()
.
- Raises:
- aggregate(qty: Key | Hashable, tag: str, dims_or_groups: Mapping | str | Sequence[str], weights: DataArray | None = None, keep: bool = True, sums: bool = False, fail: str | int | None = None)[source]#
Add a computation that aggregates qty.
- Parameters:
qty (
Key
or str) – Key of the quantity to be aggregated.tag (str) – Additional string to add to the end the key for the aggregated quantity.
dims_or_groups (str or iterable of str or dict) – Name(s) of the dimension(s) to sum over, or nested dict.
weights (
xarray.DataArray
, optional) – Weights for weighted aggregation.keep (bool, optional) – Passed to
computations.aggregate
.fail (str or int, optional) – Passed to
add_queue()
viaadd()
.
- Returns:
The key of the newly-added node.
- Return type:
- check_keys(*keys: str | Key, action='raise') List[str | Key] | None [source]#
Check that keys are in the Computer.
If any of keys is not in the Computer and action is “raise” (the default)
KeyError
is raised. Otherwise, a list is returned with either the key from keys, or the correspondingfull_key()
.If action is “return” (or any other value),
None
is returned on missing keys.
- configure(path: Path | str | None = None, fail: str | int = 'raise', config: Mapping[str, Any] | None = None, **config_kw)[source]#
Configure the Computer.
Accepts a path to a configuration file and/or keyword arguments. Configuration keys loaded from file are superseded by keyword arguments. Messages are logged at level
logging.INFO
if config contains unhandled sections.See Configuration for a list of all configuration sections and keys, and details of the configuration file format.
- Parameters:
path (.Path, optional) – Path to a configuration file in JSON or YAML format.
fail (“raise” or str or
logging
level, optional) – Passed toadd_queue()
. If not “raise”, then log messages are generated for config handlers that fail. The Computer may be only partially configured.config – Configuration keys/sections and values, as a mapping. Use this if any of the keys/sections are not valid Python names, e.g. contain “-” or “ “.
**config_kw – Configuration keys/sections and values, as keyword arguments.
- describe(key=None, quiet=True)[source]#
Return a string describing the computations that produce key.
If key is not provided, all keys in the Computer are described.
Unless quiet, the string is also printed to the console.
- Returns:
Description of computations.
- Return type:
- disaggregate(qty, new_dim, method='shares', args=[])[source]#
Add a computation that disaggregates qty using method.
- Parameters:
qty (hashable) – Key of the quantity to be disaggregated.
new_dim (str) – Name of the new dimension of the disaggregated variable.
method (callable or str) – Disaggregation method. If a callable, then it is applied to var with any extra args. If a string, then a method named ‘disaggregate_{method}’ is used.
args (list, optional) – Additional arguments to the method. The first element should be the key for a quantity giving shares for disaggregation.
- Returns:
The key of the newly-added node.
- Return type:
- full_key(name_or_key: Key | Hashable) Key | Hashable [source]#
Return the full-dimensionality key for name_or_key.
An quantity ‘foo’ with dimensions (a, c, n, q, x) is available in the Computer as
'foo:a-c-n-q-x'
. ThisKey
can be retrieved with:c.full_key("foo") c.full_key("foo:c") # etc.
- Raises:
KeyError – if name_or_key is not in the graph.
- get(key=None)[source]#
Execute and return the result of the computation key.
Only key and its dependencies are computed.
- Parameters:
key (str, optional) – If not provided,
default_key
is used.- Raises:
ValueError – If key and
default_key
are bothNone
.
- get_comp(name) Callable | None [source]#
Return a computation function.
get_comp()
checks each of themodules
for a function or callable with the given name. Modules at the end of the list take precedence over those earlier in the lists.- Returns:
.callable
None – If there is no computation with the given name in any of
modules
.
- infer_keys(key_or_keys: Key | Hashable | Iterable[Key | Hashable], dims: Iterable[str] = [])[source]#
Infer complete key_or_keys.
Each return value is one of:
a
Key
with eitherdimensions dims, if any are given, otherwise
its full dimensionality (cf.
full_key()
)
str
, the same as input, if the key is not defined in the Computer.
- modules: MutableSequence[module] = []#
List of modules containing pre-defined computations.
By default, this includes the
genno
built-in computations ingenno.computations
.require_compat()
appends additional modules, e.g. #:compat.pyam.computations
, to this list. User code may also add modules to this list.
- require_compat(pkg: str | module)[source]#
Register computations from
genno.compat
/others forget_comp()
.The specified module is appended to
modules
.- Parameters:
pkg (str or module) –
One of:
the name of a package (e.g. “plotnine”), corresponding to a submodule of
genno.compat
, e.g.genno.compat.plotnine
.genno.compat.{pkg}.computations
is added.the name of an arbitary module, e.g. “foo.bar”
a previously imported module object.
- Raises:
ModuleNotFoundError – If the required packages are missing.
Examples
Computations packaged with genno for compatibility:
>>> c = Computer() >>> c.require_compat("pyam")
Computations in another module, using the module name:
>>> c.require_compat("ixmp.reporting.computations")
or using imported module:
>>> import ixmp.reporting.computations as mod >>> c.require_compat(mod)
- property unit_registry#
The
pint.UnitRegistry()
used by the Computer.
- visualize(filename, key=None, optimize_graph=False, **kwargs)[source]#
Generate an image describing the Computer structure.
This is similar to
dask.visualize()
; seecompat.graphviz.visualize()
. Requires graphviz.
- class genno.Key(name: str, dims: Iterable[str] = [], tag: str | None = None)[source]#
A hashable key for a quantity that includes its dimensionality.
Quantities are indexed by 0 or more dimensions. A Key refers to a quantity using three components:
For example, for a \(\text{foo}\) with with three dimensions \(a, b, c\):
\[\text{foo}^{abc}\]Key allows a specific, explicit reference to various forms of “foo”:
in its full resolution, i.e. indexed by a, b, and c:
>>> k1 = Key("foo", ["a", "b", "c"]) >>> k1 <foo:a-b-c>
in a partial sum over one dimension, e.g. summed across dimension c, with remaining dimensions a and b:
>>> k2 = k1.drop('c') >>> k2 == 'foo:a-b' True
in a partial sum over multiple dimensions, etc.:
>>> k1.drop('a', 'c') == k2.drop('a') == 'foo:b' True
after it has been manipulated by other computations, e.g.
>>> k3 = k1.add_tag('normalized') >>> k3 <foo:a-b-c:normalized> >>> k4 = k3.add_tag('rescaled') >>> k4 <foo:a-b-c:normalized+rescaled>
Notes:
A Key has the same hash, and compares equal to its
str
representation. A Key also compares equal to another key orstr
with the same dimensions in any other order.repr(key)
prints the Key in angle brackets (‘<>’) to signify that it is a Key object.>>> str(k1) 'foo:a-b-c' >>> repr(k1) '<foo:a-b-c>' >>> hash(k1) == hash("foo:a-b-c") True >>> k1 == "foo:c-b-a" True
Keys are immutable: the properties
name
,dims
, andtag
are read-only, and the methodsappend()
,drop()
, andadd_tag()
return new Key objects.Keys may be generated concisely by defining a convenience method:
>>> def foo(dims): >>> return Key('foo', dims.split()) >>> foo('a b c') <foo:a-b-c>
- classmethod bare_name(value) str | None [source]#
If value is a bare name (no dims or tags), return it; else
None
.
- classmethod from_str_or_key(value: Key | Hashable, drop: Iterable[str] | bool = [], append: Iterable[str] = [], tag: str | None = None) Key [source]#
Return a new Key from value.
- Parameters:
drop (list of str or
True
, optional) – Existing dimensions of value to drop. Seedrop()
.append (list of str, optional.) – New dimensions to append to the returned Key. See
append()
.tag (str, optional) – Tag for returned Key. If value has a tag, the two are joined using a ‘+’ character. See
add_tag()
.
- Return type:
- iter_sums() Generator[Tuple[Key, Callable, Key], None, None] [source]#
Generate (key, task) for all possible partial sums of the Key.
- classmethod product(new_name: str, *keys, tag: str | None = None) Key [source]#
Return a new Key that has the union of dimensions on keys.
Dimensions are ordered by their first appearance:
First, the dimensions of the first of the keys.
Next, any additional dimensions in the second of the keys that were not already added in step 1.
etc.
- Parameters:
new_name (str) – Name for the new Key. The names of keys are discarded.
- class genno.Quantity(*args, **kwargs)[source]#
A sparse data structure that behaves like
xarray.DataArray
.Depending on the value of
CLASS
, Quantity is eitherAttrSeries
orSparseDataArray
.- classmethod from_series(series, sparse=True)[source]#
Convert series to the Quantity class given by
CLASS
.
- property units#
Retrieve or set the units of the Quantity.
Examples
Create a quantity without units:
>>> qty = Quantity(...)
Set using a string; automatically converted to pint.Unit:
>>> qty.units = "kg" >>> qty.units <Unit('kilogram')>
The Quantity
constructor converts its arguments to an internal, xarray.DataArray
-like data format:
# Existing data
data = pd.Series(...)
# Convert to a Quantity for use in reporting calculations
qty = Quantity(data, name="Quantity name", units="kg")
rep.add("new_qty", qty)
Common genno
usage, e.g. in message_ix
, creates large, sparse data frames (billions of possible elements, but <1% populated); DataArray
’s default, ‘dense’ storage format would be too large for available memory.
Currently, Quantity is
AttrSeries
, a wrappedpandas.Series
that behaves like aDataArray
.In the future,
genno
will useSparseDataArray
, and eventuallyDataArray
backed by sparse data, directly.
The goal is that all genno
-based code, including built-in and user computations, can treat quantity arguments as if they were DataArray
.
Computations#
Elementary computations for genno.
Unless otherwise specified, these methods accept and return Quantity
objects for data arguments/return values.
Genno’s compatibility modules each provide additional computations.
Numerical calculations:
|
Sum across multiple quantities. |
|
Aggregate quantity by groups. |
|
Broadcast quantity using a map. |
|
Sum distinct quantities by weights. |
|
Disaggregate quantity by shares. |
|
Compute the ratio numerator / denominator. |
|
Group by dimension group, then sum across dimension sum. |
|
Compute an index of qty against certain of its values. |
|
Interpolate qty. |
|
Compute the product of any number of quantities. |
|
Compute a raised to the power of b. |
|
Alias of |
|
Alias of |
|
Sum quantity over dimensions, with optional weights. |
Input and output:
|
Read the file at path and return its contents as a |
|
Write a quantity to a file. |
Data manipulation:
|
Apply units to qty. |
|
Set the units of qty without changing magnitudes. |
|
Concatenate Quantity objs. |
|
Convert magnitude of qty from its current units to units. |
|
Replace specific labels along dimensions of qty. |
|
Rename the dimensions of qty. |
|
Select from qty based on indexers. |
- genno.computations.add(*quantities: Quantity, fill_value: float = 0.0) Quantity [source]#
Sum across multiple quantities.
- Raises:
ValueError – if any of the quantities have incompatible units.
- Returns:
Units are the same as the first of quantities.
- Return type:
.Quantity
- genno.computations.aggregate(quantity: Quantity, groups: Mapping[str, Mapping], keep: bool) Quantity [source]#
Aggregate quantity by groups.
- Parameters:
groups (dict of dict) – Top-level keys are the names of dimensions in quantity. Second-level keys are group names; second-level values are lists of labels along the dimension to sum into a group.
keep (bool) – If True, the members that are aggregated into a group are returned with the group sums. If False, they are discarded.
- Returns:
Same dimensionality as quantity.
- Return type:
Quantity
- genno.computations.apply_units(qty: Quantity, units: str | Unit | Quantity) Quantity [source]#
Apply units to qty.
If qty has existing units…
…with compatible dimensionality to units, the magnitudes are adjusted, i.e. behaves like
convert_units()
.…with incompatible dimensionality to units, the units attribute is overwritten and magnitudes are not changed, i.e. like
assign_units()
, with a log message on levelWARNING
.
To avoid ambiguities between the two cases, use
convert_units()
orassign_units()
instead.
- genno.computations.assign_units(qty: Quantity, units: str | Unit | Quantity) Quantity [source]#
Set the units of qty without changing magnitudes.
Logs on level
INFO
if qty has existing units.
- genno.computations.broadcast_map(quantity: Quantity, map: Quantity, rename: Mapping = {}, strict: bool = False) Quantity [source]#
Broadcast quantity using a map.
The map must be a 2-dimensional Quantity with dimensions (
d1
,d2
), such as returned bymap_as_qty()
. quantity must also have a dimensiond1
. Typicallylen(d2) > len(d1)
.quantity is ‘broadcast’ by multiplying it with map, and then summing on the common dimension
d1
. The result has the dimensions of quantity, but withd2
in place ofd1
.
- genno.computations.combine(*quantities: Quantity, select: List[Mapping] | None = None, weights: List[float] | None = None) Quantity [source]#
Sum distinct quantities by weights.
- Parameters:
- Raises:
ValueError – If the quantities have mismatched units.
- genno.computations.concat(*objs: Quantity, **kwargs) Quantity [source]#
Concatenate Quantity objs.
Any strings included amongst objs are discarded, with a logged warning; these usually indicate that a quantity is referenced which is not in the Computer.
- genno.computations.convert_units(qty: Quantity, units: str | Unit | Quantity) Quantity [source]#
Convert magnitude of qty from its current units to units.
- Parameters:
- Raises:
ValueError – if units does not match the dimensionality of the current units of qty.
Disaggregate quantity by shares.
- genno.computations.div(numerator: Quantity | float, denominator: Quantity) Quantity [source]#
Compute the ratio numerator / denominator.
- Parameters:
numerator (.Quantity) –
denominator (.Quantity) –
- genno.computations.drop_vars(qty: Quantity, names: Hashable | Iterable[Hashable], *, errors='raise') Quantity [source]#
Return a Quantity with dropped variables (coordinates).
- genno.computations.group_sum(qty: Quantity, group: str, sum: str) Quantity [source]#
Group by dimension group, then sum across dimension sum.
The result drops the latter dimension.
- genno.computations.index_to(qty: Quantity, dim_or_selector: str | Mapping, label: Hashable | None = None) Quantity [source]#
Compute an index of qty against certain of its values.
If the label is not provided,
index_to()
uses the label in the first position along the identified dimension.- Parameters:
- Raises:
TypeError – if dim_or_selector is a mapping with length != 1.
- genno.computations.interpolate(qty: Quantity, coords: Mapping[Hashable, Any] | None = None, method: Literal['linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial'] | Literal['barycentric', 'krog', 'pchip', 'spline', 'akima'] = 'linear', assume_sorted: bool = True, kwargs: Mapping[str, Any] | None = None, **coords_kwargs: Any) Quantity [source]#
Interpolate qty.
For the meaning of arguments, see
xarray.DataArray.interp()
. WhenCLASS
isAttrSeries
, only 1-dimensional interpolation (one key in coords) is tested/supported.
- genno.computations.load_file(path: Path, dims: Collection[Hashable] | Mapping[Hashable, Hashable] = {}, units: str | Unit | Quantity | None = None, name: str | None = None) Any [source]#
Read the file at path and return its contents as a
Quantity
.Some file formats are automatically converted into objects for direct use in genno computations:
.csv
:Converted to
Quantity
. CSV files must have a ‘value’ column; all others are treated as indices, except as given by dims. Lines beginning with ‘#’ are ignored.
- Parameters:
path (pathlib.Path) – Path to the file to read.
dims (collections.abc.Collection or collections.abc.Mapping, optional) – If a collection of names, other columns besides these and ‘value’ are discarded. If a mapping, the keys are the column labels in path, and the values are the target dimension names.
units (str or pint.Unit) – Units to apply to the loaded Quantity.
name (str) – Name for the loaded Quantity.
- genno.computations.mul(*quantities: Quantity) Quantity [source]#
Compute the product of any number of quantities.
- genno.computations.pow(a: Quantity, b: Quantity | int) Quantity [source]#
Compute a raised to the power of b.
Todo
Provide units on the result in the special case where b is a Quantity but all its values are the same
int
.- Returns:
If b is
int
, then the quantity has the units of a raised to this power; e.g. “kg²” → “kg⁴” if b is 2. In other cases, there are no meaningful units, so the returned quantity is dimensionless.- Return type:
.Quantity
- genno.computations.product(*quantities: Quantity) Quantity #
Alias of
mul()
, for backwards compatibility.Note
This may be deprecated and possibly removed in a future version.
- genno.computations.ratio(numerator: Quantity | float, denominator: Quantity) Quantity #
Alias of
div()
, for backwards compatibility.Note
This may be deprecated and possibly removed in a future version.
- genno.computations.relabel(qty: Quantity, labels: Mapping[Hashable, Mapping] | None = None, **dim_labels: Mapping) Quantity [source]#
Replace specific labels along dimensions of qty.
- Parameters:
labels – Keys are strings identifying dimensions of qty; values are further mappings from original labels to new labels. Dimensions and labels not appearing in qty have no effect.
dim_labels – Mappings given as keyword arguments, where argument name is the dimension.
- Raises:
ValueError – if both labels and dim_labels are given.
- genno.computations.rename_dims(qty: Quantity, new_name_or_name_dict: Hashable | Mapping[Any, Hashable] | None = None, **names: Hashable) Quantity [source]#
Rename the dimensions of qty.
- genno.computations.round(qty: Quantity, *args, **kwargs) Quantity [source]#
Like
xarray.DataArray.round()
.
- genno.computations.select(qty: Quantity, indexers: Mapping[Hashable, Iterable[Hashable]], *, inverse: bool = False, drop: bool = False) Quantity [source]#
Select from qty based on indexers.
- Parameters:
indexers (dict (str -> xarray.DataArray or list of str)) – Elements to be selected from qty. Mapping from dimension names to coords along the respective dimension of qty, or to xarray-style indexers. Values not appearing in the dimension coords are silently ignored.
inverse (bool, optional) – If
True
, remove the items in indexers instead of keeping them.
Internal format for quantities#
- genno.core.quantity.assert_quantity(*args)[source]#
Assert that each of args is a Quantity object.
- Raises:
TypeError – with a indicative message.
- genno.core.quantity.maybe_densify(func)[source]#
Wrapper for computations that densifies
SparseDataArray
input.
- class genno.core.attrseries.AttrSeries(*args, **kwargs)[source]#
pandas.Series
subclass imitatingxarray.DataArray
.The AttrSeries class provides similar methods and behaviour to
xarray.DataArray
, so thatgenno.computations
functions and user code can use xarray-like syntax. In particular, this allows such code to be agnostic about the order of dimensions.- Parameters:
- name#
The name of this Quantity.
Like
xarray.DataArray.name
.
- align_levels(other: AttrSeries) Tuple[Sequence[Hashable], AttrSeries] [source]#
Return a copy of self with ≥1 dimension(s) in the same order as other.
Work-around for pandas-dev/pandas#25760 and other limitations of
pandas.Series
.
- property coords#
Like
xarray.DataArray.coords
. Read-only.
- property dims: Tuple[Hashable, ...]#
Like
xarray.DataArray.dims
.
- drop(label)[source]#
Like
xarray.DataArray.drop()
.
- expand_dims(dim=None, axis=None, **dim_kwargs: Any) AttrSeries [source]#
- interp(coords: Mapping[Hashable, Any] | None = None, method: str = 'linear', assume_sorted: bool = True, kwargs: Mapping[str, Any] | None = None, **coords_kwargs: Any)[source]#
Like
xarray.DataArray.interp()
.This method works around two long-standing bugs in
pandas
:
- item(*args)[source]#
Like
xarray.DataArray.item()
.
- rename(new_name_or_name_dict: Hashable | Mapping[Hashable, Hashable] | None = None, **names: Hashable)[source]#
- sel(indexers: Mapping[Any, Any] | None = None, method: str | None = None, tolerance=None, drop: bool = False, **indexers_kwargs: Any)[source]#
Like
xarray.DataArray.sel()
.
- property shape: Tuple[int, ...]#
Like
xarray.DataArray.shape
.
- shift(shifts: Mapping[Hashable, int] | None = None, fill_value: Any | None = None, **shifts_kwargs: int)[source]#
Like
xarray.DataArray.shift()
.
- sum(dim: str | Iterable[Hashable] | None = None, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any) AttrSeries [source]#
Like
xarray.DataArray.sum()
.
- to_dataframe(name: Hashable | None = None, dim_order: Sequence[Hashable] | None = None) DataFrame [source]#
- class genno.core.sparsedataarray.SparseAccessor(obj)[source]#
xarray
accessor to helpSparseDataArray
.See the xarray accessor documentation, e.g.
register_dataarray_accessor()
.- convert()[source]#
Return a
SparseDataArray
instance.
- property dense#
Return a copy with dense (
ndarray
) data.
- property dense_super#
Return a proxy to a
ndarray
-backedDataArray
.
- class genno.core.sparsedataarray.SparseDataArray(*args, **kwargs)[source]#
DataArray
with sparse data.SparseDataArray uses
sparse.COO
for storage withnumpy.nan
as itssparse.COO.fill_value
. Some methods ofDataArray
are overridden to ensure data is in sparse, or dense, format as necessary, to provide expected functionality not currently supported bysparse
, and to avoid exhausting memory for some operations that require dense data.- sel(indexers: Mapping[Any, Any] | None = None, method: str | None = None, tolerance=None, drop: bool = False, **indexers_kwargs: Any) SparseDataArray [source]#
Return a new array by selecting labels along the specified dim(s).
Overrides
sel()
to handle >1-D indexers with sparse data.
- to_dataframe(name: Hashable | None = None, dim_order: Sequence[Hashable] | None = None) DataFrame [source]#
Convert this array and its coords into a
DataFrame
.Overrides
to_dataframe()
.
- to_series() Series [source]#
Convert this array into a
Series
.Overrides
to_series()
to create the series without first converting to a potentially very largenumpy.ndarray
.
- class genno.compat.xarray.DataArrayLike[source]#
Class with
xarray.DataArray
-like API.This class is used to set signatures and types for methods and attributes on the generic
Quantity
class.SparseDataArray
inherits from both this class andDataArray
, and thus DataArray supplies implementations of these methods. InAttrSeries
, the methods are implemented directly.
Internals and utilities#
- genno.compat.graphviz.visualize(dsk: Mapping, filename: str | PathLike | None = None, format: str | None = None, data_attributes: Mapping | None = None, function_attributes: Mapping | None = None, graph_attr: Mapping | None = None, node_attr: Mapping | None = None, edge_attr: Mapping | None = None, collapse_outputs=False, **kwargs)[source]#
Generate a Graphviz visualization of dsk.
This is merged and extended version of
dask.base.visualize()
,dask.dot.dot_graph()
, anddask.dot.to_graphviz()
that produces output that is informative for genno graphs.- Parameters:
dsk – The graph to display.
filename (Path or str, optional) – The name of the file to write to disk. If the file name does not have a suffix, “.png” is used by default. If filename is
None
, no file is written, and dask communicates with dot using only pipes.format ({'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional) – Format in which to write output file, if not given by the suffix of filename. Default “png”.
data_attributes – Graphviz attributes to apply to single nodes representing keys, in addition to node_attr.
function_attributes – Graphviz attributes to apply to single nodes representing operations or functions, in addition to node_attr.
graph_attr – Mapping of (attribute, value) pairs for the graph. Passed directly to
graphviz.Digraph
.node_attr – Mapping of (attribute, value) pairs set for all nodes. Passed directly to
graphviz.Digraph
.edge_attr – Mapping of (attribute, value) pairs set for all edges. Passed directly to
graphviz.Digraph
.collapse_outputs (bool, optional) – Omit nodes for keys that are the output of intermediate calculations.
kwargs – All other keyword arguments are added to graph_attr.
Examples
Prepare a computer:
>>> from genno import Computer >>> from genno.testing import add_test_data >>> c = Computer() >>> add_test_data(c) >>> c.add_product("z", "x:t", "x:y") >>> c.add("y::0", itemgetter(0), "y") >>> c.add("y0", "y::0") >>> c.add("index_to", "z::indexed", "z:y", "y::0") >>> c.add_single("all", ["z::indexed", "t", "config", "x:t"])
Visualize its contents:
>>> c.visualize("example.svg")
This produces the output:
See also
- genno.core.describe.MAX_ITEM_LENGTH = 160#
Default maximum length for outputs from
describe_recursive()
.
- genno.core.describe.describe_recursive(graph, comp, depth=0, seen=None)[source]#
Recursive helper for
describe()
.
- genno.core.describe.is_list_of_keys(arg: Any, graph: Mapping) bool [source]#
Identify a task which is a list of other keys.
- genno.core.describe.label(arg, max_length=160) str [source]#
Return a label for arg.
The label depends on the type of arg:
xarray.DataArray
: the first line of the string representation.partial()
object: a less-verbose version that omits None arguments.Item protected with
dask.core.quote()
: its literal value.A callable, e.g. a function: its name.
Anything else: its
str
representation.
In all cases, the string is no longer than max_length.
- class genno.core.graph.Graph(*args, **kwargs)[source]#
A dictionary for a graph indexed by
Key
.Graph maintains indexes on set/delete/pop/update operations that allow for fast lookups/member checks in certain special cases:
unsorted_key
(key)Return key with its original or unsorted dimensions.
full_key
(name_or_key)Return name_or_key with its full dimensions.
These basic features are used to provide higher-level helpers for
Computer
:infer
(key[, dims])Infer a key.
- full_key(name_or_key: Key | Hashable) Key | Hashable | None [source]#
Return name_or_key with its full dimensions.
- infer(key: str | Key, dims: Iterable[str] = []) Key | Hashable | None [source]#
Infer a key.
- Parameters:
dims (list of str, optional) – Drop all but these dimensions from the returned key(s).
- Returns:
str – If key is not found in the Graph.
Key – key with either its full dimensions (cf.
full_key()
) or, if dims are given, with only these dims.
- pop(k[, d]) v, remove specified key and return the corresponding value. [source]#
If the key is not found, return the default if given; otherwise, raise a KeyError.
- genno.util.REPLACE_UNITS = {'%': 'percent'}#
Replacements to apply to Quantity units before parsing by pint. Mapping from original unit -> preferred unit.
The default values include:
The ‘%’ symbol cannot be supported by pint, because it is a Python operator; it is replaced with “percent”.
Additional values can be added with
configure()
; see units:.
- genno.util.clean_units(input_string)[source]#
Tolerate messy strings for units.
Dimensions enclosed in “[]” have these characters stripped.
Replacements from
REPLACE_UNITS
are applied.
- genno.util.filter_concat_args(args)[source]#
Filter out str and Key from args.
A warning is logged for each element removed.
- genno.util.parse_units(data: Iterable, registry=None) Unit [source]#
Return a
pint.Unit
for an iterable of strings.Valid unit expressions not already present in the registry are defined, e.g.:
u = parse_units(["foo/bar", "foo/bar"], reg)
…results in the addition of unit definitions equivalent to:
reg.define("foo = [foo]") reg.define("bar = [bar]") u = reg.foo / reg.bar
- Raises:
ValueError – if data contains more than 1 unit expression, or the unit expression contains characters not parseable by
pint
, e.g.-?$
.
Utilities for testing#
- genno.testing.add_dantzig(c: Computer)[source]#
Add contents analogous to the ixmp Dantzig scenario.
- genno.testing.add_large_data(c: Computer, num_params, N_dims=6, N_data=0)[source]#
Add nodes to c that return large-ish data.
The result is a matrix wherein the Cartesian product of all the keys is very large— about 2e17 elements for N_dim = 6—but the contents are very sparse. This can be handled by
SparseDataArray
, but not byxarray.DataArray
backed bynp.array
.
- genno.testing.add_test_data(c: Computer)[source]#
add_test_data()
operating on a Computer, not an ixmp.Scenario.
- genno.testing.assert_logs(caplog, message_or_messages=None, at_level=None)[source]#
Assert that message_or_messages appear in logs.
Use assert_logs as a context manager for a statement that is expected to trigger certain log messages. assert_logs checks that these messages are generated.
Derived from
ixmp.testing.assert_logs()
.Example
>>> def test_foo(caplog): ... with assert_logs(caplog, 'a message'): ... logging.getLogger(__name__).info('this is a message!')
- genno.testing.assert_qty_allclose(a, b, check_type: bool = True, check_attrs: bool = True, ignore_extra_coords: bool = False, **kwargs)[source]#
Assert that objects a and b have numerically close values.
- Parameters:
check_type (bool, optional) – Assert that a and b are both
Quantity
instances. IfFalse
, the arguments are converted to Quantity.check_attrs (bool, optional) – Also assert that check that attributes are identical.
ignore_extra_coords (bool, optional) – Ignore extra coords that are not dimensions. Only meaningful when Quantity is
SparseDataArray
.
- genno.testing.assert_qty_equal(a, b, check_type: bool = True, check_attrs: bool = True, ignore_extra_coords: bool = False, **kwargs)[source]#
Assert that objects a and b are equal.
- Parameters:
check_type (bool, optional) – Assert that a and b are both
Quantity
instances. IfFalse
, the arguments are converted to Quantity.check_attrs (bool, optional) – Also assert that check that attributes are identical.
ignore_extra_coords (bool, optional) – Ignore extra coords that are not dimensions. Only meaningful when Quantity is
SparseDataArray
.
- genno.testing.pytest_runtest_makereport(item, call)[source]#
Pytest hook to unwrap
genno.ComputationError
.This allows to “xfail” tests more precisely on the underlying exception, rather than the ComputationError which wraps it.
- genno.testing.random_qty(shape: Dict[str, int], **kwargs)[source]#
Return a Quantity with shape and random contents.
- Parameters:
shape (dict (str -> int)) – Mapping from dimension names to lengths along each dimension.
**kwargs – Other keyword arguments to
Quantity
.
- Returns:
Random data with one dimension for each key in shape, and coords along those dimensions like “foo1”, “foo2”, with total length matching the value from shape. If shape is empty, a scalar (0-dimensional) Quantity.
- Return type: