reno.ops.interpolate#

class reno.ops.interpolate(x, x_data, y_data)#

Given a dataset of x -> y datapoints, interpolate any new data along the line formed by the points. Equivalent to numpy’s interp function.

String notation: (interpolate X X_DATA Y_DATA)

Parameters:

x – The input x-coordinates that you want interpolated into y outputs.
x_data (list | ndarray) – The x-coordinate data to base interpolation on.
y_data (list | ndarray) – the y-coordinate data to base interpolation on.

Methods

`__init__`(x, x_data, y_data)
`astype`(dtype)
`clip`(min, max)
`equal`(obj)
`eval`([t, save, force])	'wrap' all operation eval functions so we get better error handling.
`find_refs_of_type`(search_type[, already_checked])	Actually recursive as opposed to seek_refs, returns a list of all equation parts matching passed type.
`get_shape`()	The shapes of x_data and y_data don't matter, should be same as input.
`get_type`()	Similar to shape, this gets computed recursively, used to automatically determine if the value needs to be initialized with a certain numpy type.
`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)	Wrapper around op_latex, which subclasses should override
`mean`([axis])
`not_equal`(obj)
`op_eval`(**kwargs)	Any new operations should implement this method.
`op_latex`(**kwargs)	Override this in subclasses
`op_repr`()	Get a string representation for the op name/label, used for printing and parsing.
`op_types`()	Get a list of classes that inherit from this one, in other words all of the possible defined operations.
`pt`(**refs)	Get a pytensor graph representing this piece of an equation.
`pt_str`(**refs)	Construct a string containing relevant pytensor code for this piece of the equation.
`seek_refs`([include_ref_types])	Immediate refs only, depth=1.
`series_max`()
`series_min`()
`sum`([axis])

Attributes

`OP_REPR`	If defined, this is what gets printed as the 'label' for the operation in the repr, and is what's used during parsing.
`dtype`	The type of each underlying value.
`shape`	The size of the data dimension, 1 by default.
`timeseries`	Get a timeseries view of the data (includes all historical data across all timesteps.)
`non_repr_part_indices`	Use this to "hide" a sub_equation_part in the string repr, e.g. if it's important for computation but isn't part of parsing.

get_shape()#

The shapes of x_data and y_data don’t matter, should be same as input.

op_eval(**kwargs)#: Any new operations should implement this method. (Put all evaluation logic in here, as opposed to overriding eval.)

pt(**refs)#

Get a pytensor graph representing this piece of an equation.

pt_str(**refs)#

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