# Derived timeseries data¶

class pyam.IamComputeAccessor(df)[source]

Perform computations on the timeseries data of an IamDataFrame

An IamDataFrame has a module for computation of (advanced) indicators from the timeseries data.

The methods in this module can be accessed via

IamDataFrame.compute.<method>(*args, **kwargs)


Methods

 growth_rate(mapping[, append]) Compute the annualized growth rate of a timeseries along the time dimension learning_rate(name, performance, experience) Compute the implicit learning rate from timeseries data
growth_rate(mapping, append=False)[source]

Compute the annualized growth rate of a timeseries along the time dimension

The growth rate parameter in period t is computed based on the changes to the subsequent period, i.e., from period t to period t+1.

Parameters
mappingdict

Mapping of variable item(s) to the name(s) of the computed data, e.g.,

{"variable": "name of growth-rate variable", ...}

appendbool, optional

Whether to append computed timeseries data to this instance.

Returns
IamDataFrame or None

Computed timeseries data or None if append=True.

Raises
ValueError

Math domain error when timeseries crosses 0.

learning_rate(name, performance, experience, append=False)[source]

Compute the implicit learning rate from timeseries data

Experience curves are based on the concept that a technology’s performance improves as experience with this technology grows.

The “learning rate” indicates the performance improvement (e.g., cost reduction) for each doubling of the accumulated experience (e.g., cumulative capacity).

The experience curve parameter b is equivalent to the (linear) slope when plotting performance and experience timeseries on double-logarithmic scales. The learning rate can be computed from the experience curve parameter as $$1 - 2^{b}$$.

The learning rate parameter in period t is computed based on the changes to the subsequent period, i.e., from period t to period t+1.

Parameters
namestr

Variable name of the computed timeseries data.

performancestr

Variable of the “performance” timeseries (e.g., specific investment costs).

experiencestr

Variable of the “experience” timeseries (e.g., installed capacity).

appendbool, optional

Whether to append computed timeseries data to this instance.

Returns
IamDataFrame or None

Computed timeseries data or None if append=True.