PyFoam.Basics.Data2DStatistics module¶
Data structure to do some calculations on the results from SpreadSheetData-methods metrics and compare that are organized in 2 dimensions
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class
PyFoam.Basics.Data2DStatistics.
Data2DStatistics
(metrics, compare=None, small=1e-10, noStrings=False, failureValue=None)[source]¶ Bases:
object
Oranize statistics about data in 2D-Tables and do basic calculations on it
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_Data2DStatistics__relativeErrorInternal
(name)¶ Return a table with the relative error :param name: spcifies the name under which the error is found in the data
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__dict__
= dict_proxy({'__module__': 'PyFoam.Basics.Data2DStatistics', '_extractTable': <function _extractTable>, '__getitem__': <function __getitem__>, 'relativeError': <function relativeError>, '_Data2DStatistics__relativeErrorInternal': <function __relativeErrorInternal>, 'compare': <function compare>, '_getLabels': <function _getLabels>, 'relativeAverageError': <function relativeAverageError>, 'range': <function range>, 'names': <function names>, 'func': <function func>, '_makeEmptyTable': <function _makeEmptyTable>, '__dict__': <attribute '__dict__' of 'Data2DStatistics' objects>, '__weakref__': <attribute '__weakref__' of 'Data2DStatistics' objects>, '__doc__': 'Oranize statistics about data in 2D-Tables and do basic\n calculations on it', '__init__': <function __init__>})¶
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__init__
(metrics, compare=None, small=1e-10, noStrings=False, failureValue=None)[source]¶ Parameters: - metrics – metrics of the data
- compare – metrics of the comparsion with another data-set
- small – the value that is considered to be close to 0
- noStrings – only put numbers into the tables
- failureValue – the value to use if an evaluation fails
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__module__
= 'PyFoam.Basics.Data2DStatistics'¶
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__weakref__
¶ list of weak references to the object (if defined)
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_extractTable
(name, data=None)[source]¶ Extract data and fill it into a data-table :param name: name of the entry that should be got :param data: the dataset. If unset then self.__metrics is used
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_getLabels
()[source]¶ Return a tuple with the names of the rows and the columns. Assumes that the names for the first data-set are valid for all
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