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 NoneComputed timeseries data or None if append=True.
- Raises
- ValueError
Math domain error when timeseries crosses 0.
See also
- 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 NoneComputed timeseries data or None if append=True.