identify.static

StaticIdentificier

StaticIdentificier

Identify components of which the static results differ between softwares.

Identify submodule providing identification utilities for static results.

class tessif.identify.static.StaticIdentificier(data, conditions_dict=None, reference=None)

Bases: Identificier

Identify components of which the static results differ between softwares.

Components are identified using following logic:

Parameters:

• data (pandas.DataFrame) –

  Pandas DataFrame holding the static results. Indexed by components/flows, columned by softwares.

  DataFrames of this format can be obtained using tessif.analyze.ComparativeResultier.all_capacities or tessif.analyze.ComparativeResultier.all_original_capacities or tessif.analyze.ComparativeResultier.all_net_energy_flows for example.

• conditions_dict (dict, default=None) –

  Dictionairy describing the clustering categories as strings and the respective threshold above which a difference between softwares is considered to fall within this cluster.

  The dict keys container(s) of dicts by the respective cluster labels “high”, “medium” and “low”. The dictionairies inside the tuples need to have following keywords:

  If None, following default is used:

  conditions_dict = {
      "high": (
          {"oprt": "ge", "thres": 0.3,
          {"oprt": "ge", "thres": 0.3},
      ),
      "medium": (
          {"oprt": "lt", "thres": 0.3},
          {"oprt": "ge", "thres": 0.1},
      ),
      "low": (
          {"oprt": "lt", "thres": 0.1},
          {"oprt": "lt", "thres": 0.1},
      ),
  }
  

  which translates to:

  • high: 0.3 <= delta

  • medium: 0.1 <= delta < 0.3

  • low: 0.0 <= dleta < 0.1

• reference (str, None, default=None) –

  Defines the reference results to be used for calculating the absolute relative deviation between softwares.

  In case None is used (default), the dataframes average is used as returned by average_timevarying_dataframe_results().

property relative_deviations

Relative deviations between data and reference.

cluster_interest()

Cluster inter component results by interest.

map_interest_results(data)

Map data to identified interest categories.