Diagnostics

historical.diagnostic contains two kinds of diagnostics, which are descriptions of part or all of a data set:

  • coverage concerns which areas (countries or regions), time periods, and measures are included, or not.

  • quality includes sanity checks, such as computed/derived statistics for data, and their comparison to reference values.

Automated diagnostics

These can be run using the CLI command ixmp historical diagnostic FOLDER. Output is produced in a new folder named FOLDER.

Diagnostics for historical data sets.

item.historical.diagnostic.coverage(df, area='COUNTRY', measure='VARIABLE', period='TIME_PERIOD')[source]

Return information about the coverage of a data set.

item.historical.diagnostic.run_all(output_path)[source]

Run all diagnostics.

Specific diagnostic tests

A001

item.historical.diagnostic.A001.ARGS = ['T000']

Input arguments

item.historical.diagnostic.A001.compute(activity: DataFrame) → DataFrame[source]

Quality diagnostic for road share of passenger activity.

Parameters:

activity (pandas.DataFrame) – From T000.

A002

item.historical.diagnostic.A002.ARGS = ['T000', 'T008']

Input arguments

item.historical.diagnostic.A002.compute(activity: DataFrame, stock: DataFrame) → DataFrame[source]

Quality diagnostic for vehicle utilization.

Parameters:

A003

item.historical.diagnostic.A003.ARGS = ['T003', 'T009']

Input arguments

item.historical.diagnostic.A003.compute(activity, stock)[source]

Quality diagnostic for freight load factor.

Returns the ratio of road freight traffic from T003 and the total number of freight vehicles from T009.

Parameters: