reno.components.Piecewise#

class reno.components.Piecewise(equations, conditions)#

Bases: EquationPart

A conditional allowing evaluation of two or more condition equations to determine which output equation to use.

Mathematically allows something like:

⎧ 1 if t < 4

f(t) = ⎨ 5 if 4 <= t < 7

⎩ 9 + t if 7 <= t

Methods

__init__(equations, conditions)

Create a piecewise ("if-else operation") node.

astype(dtype)

Returns a symbolic operation to convert the output of this equation to the specified type.

clip(min, max)

Returns a symbolic operation for enforcing the output is between the passed min and max.

equal(obj)

Returns a symbolic operation for checking equality with passed object.

eval([t, save, force])

Evaluate condition equations/functions until a True is returned, and then evaluate and return the corresponding equation.

eval_condition(i, *args, **kwargs)

Recursively build nested np.where to handle all conditions.

find_parts_of_type(search_type[, ...])

Recursively search for all EquationParts in the tree of the specified type.

get_shape()

Get the size of the additional "data" dimension.

get_type()

Get the type of the target output of this equation expression.

is_static()

Convenience shortcut for reno.utils.is_static() - True if this equation doesn't rely on any dynamic values (thus constant), False if it does.

latex(**kwargs)

Get a latex piecewise string (left bracket with eqs and conditions).

mean([axis])

Returns a symbolic operation to find the series-wise mean of the array.

not_equal(obj)

Returns a symbolic operation for checking inequality with passed object.

parse(arg_strs, refs)

Convert a set of argument strings and references into a piecewise node.

pt(**refs)

Get a pytensor graph representing this piece of an equation.

pt_condition(i, refs)

Get the pytensor equation starting at the ith condition (using ifelse).

pt_condition_str(i, refs)

Get the string for the pytensor code for the ith condition (using ifelse).

pt_str(**refs)

Construct a string containing relevant pytensor code for this piece of the equation.

seek_refs([include_ref_types])

Recursively find a list of all References immediately underneath this part.

series_max()

Returns a symbolic operation to find the series-wise maximum of the array.

series_min()

Returns a symbolic operation to find the series-wise minimum of the array.

sum([axis])

Returns a symbolic operation to find the series-wise sum of the array.

Attributes

dtype

The type of each underlying value.

shape

The size of the data dimension, 1 by default.

timeseries

Returns symbolic operation for getting a timeseries view of the data.
Parameters:

  • equations (list[EquationPart])

  • conditions (list[Callable])

__annotations__ = {}#
__module__ = 'reno.components'#
__repr__()#

Return repr(self).

Return type:

str

eval(t=0, save=False, force=False, **kwargs)#

Evaluate condition equations/functions until a True is returned, and then evaluate and return the corresponding equation.

Parameters:

  • t (int)

  • save (bool)

  • force (bool)

  • kwargs (dict)

Return type:

int | float | ndarray

eval_condition(i, *args, **kwargs)#

Recursively build nested np.where to handle all conditions.

Note that this does evaluate all equations and all conditions, there are likely ways to improve efficiency/reduce unnecessary compute (e.g. this used to use np.piecewise, but adding data dimensions significantly increased complexity)

Parameters:

  • i (int)

  • args (list)

  • kwargs (dict)

Return type:

bool | ndarray

get_shape()#

Get the size of the additional “data” dimension.

Prefer using shape property over directly calling this function.

“Broadcasts” any size 1 dims to a larger dim node if present.

Return type:

int

get_type()#

Get the type of the target output of this equation expression.

Prefer using dtype property over directly calling this function.

Return type:

type

latex(**kwargs)#

Get a latex piecewise string (left bracket with eqs and conditions).

Conditions that aren’t EquationParts can’t really be converted meaningfully, so are represented simply with \\lambda(t).

Parameters:

kwargs (dict)

Return type:

str

static parse(arg_strs, refs)#

Convert a set of argument strings and references into a piecewise node.

Parsing a piecewise has to be handled differently from normal operations because of the collection of associated arguments.

Parameters:

  • arg_strs (list[str])

  • refs (dict[str, Reference])

Return type:

Piecewise

pt(**refs)#

Get a pytensor graph representing this piece of an equation.

Parameters:

refs (dict[str, TensorVariable])

Return type:

TensorVariable

pt_condition(i, refs)#

Get the pytensor equation starting at the ith condition (using ifelse).

Recursively includes all >i conditions.

Parameters:

  • i (int)

  • refs (dict[str, TensorVariable])

Return type:

TensorVariable

pt_condition_str(i, refs)#

Get the string for the pytensor code for the ith condition (using ifelse).

Recursively includes all >i conditions.

Parameters:

  • i (int)

  • refs (dict[str, str])

Return type:

str

pt_str(**refs)#

Construct a string containing relevant pytensor code for this piece of the equation. This is useful for “compiling” into pymc code.

Parameters:

refs (dict[str, str])

Return type:

str