Package dsc_suite :: Package analyses :: Module characteristics
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Module characteristics

source code

Created on 30.06.2010


Author: Robert

Functions [hide private]
 
get_statistic_characteristics(filename) source code
 
dynamic_characteristic_leveling(data, samples=0, start_index=0, characteristic_function=<function mean at 0x0280F730>) source code
 
test_dynamic_leveling(data, samples=2000, cut_away=0, trials=5, characteristic='mean value', final_value=False, sum_stats=False, create_png=False)
Plot leveling of the given characteristic.
source code
Variables [hide private]
  CHARACTERISTICS_LIST = {'mean value': lambda x: x.mean(), 'sta...
  __package__ = 'dsc_suite.analyses'
Function Details [hide private]

test_dynamic_leveling(data, samples=2000, cut_away=0, trials=5, characteristic='mean value', final_value=False, sum_stats=False, create_png=False)

source code 
Plot leveling of the given characteristic.

data ... cost criteria in chronological order
         (out of optimization, format: numpy.array)
samples ... maximum of samples considered for leveling
cut_away ... number of data points to omit (skip initial stage)
trials ... how many different trials are used
characteristic ... statistical property to investigate
final_value ... if True, outputs the statistical property
                calculated over the complete data set
sum_stats ... if True, replace trials with their mean and std values
create_png ... if True, a .png-file is created (otherwise interactive use) 

Plot the leveling of the given characteristic. X-axis represents the
number of used samples to achieve the corresponding value. If sum_stats
is True, the standard deviation and the mean value is calculated over all
trials for each number of used samples. 

To generate several trials, data is split into multiple parts. Thus, the
current approach to generate different trials is only applicable for
Monte Carlo generated date (randomly and independent at each point).

TODO!!! To investigate the leveling for other kind of data a new approach
is necessary! Until that use trials = 1 in the accordant cases. 


Variables Details [hide private]

CHARACTERISTICS_LIST

Value:
{'mean value': lambda x: x.mean(), 'standard deviation': lambda x: x.s\
td(), 'skew': skew, 'kurtosis': kurtosis, 'five percent quantile': lam\
bda x: mstats.mquantiles(x, [0.05]) [0], 'minimum': lambda x: x.min(),\
 'maximum': lambda x: x.max(), 'range': lambda x: x.max()-x.min()}