...
The Statistical functions group defines dedicated graphing options shown in the combo box in the toolbar:
calendar aggregation (with associated time step)
Creates an aggregation of a time series array according to the selected time step.
Attributes:
- function: calendarAggregation
- timeStep: Time steps that the user selects from using the slider.
relative aggregation (with associated time span)
Creates an aggregation of a time series array. A relative time step is calculated by the selected time span and the start time of the period from the time series array.
duration exceedence (i.e. sorted descending).
duration non-exceedence (i.e. sorted ascending).
Attributes:
- function: relativeAggregation
- movingAccumulationTimeSpan: Time spans that the user selects from using the slider.
duration exceedence
Attributes:
Sorts the values in a time series array and its flags in descending order.
Attributes:
- function: durationExceedence
duration non-exceedence
Attributes:
Sorts the values in a time series array and its flags in ascending order.
Attributes:
- function: durationNonExceedence
moving average (with associated time span)
A moving average calculates the mean value of the all values within the selected time window.
Attributes:
- function: movingAverage
- ignoreMissings: when true, missing values are ignored and each average will be calculated from the available values within the current time window.
When false, calculated values will be set to missing if one or more values within the current time window are missing. - movingAccumulationTimeSpan: Time spans that the user selects from using the slider.
The moving average function only works for true equidistant data (i.e. no daysOfMonths etc.)
The difference between moving average and central moving average is that the central moving average uses values before and after the current value to calculate the average. Moving average only uses values in the past.
central moving average (with associated time span)
...
A central moving average calculates the mean value of the time window of which the current value is in the middle. It is the same as the moving average, but shifted to the past for half the time window.
Attributes:
- function: always central_moving_average.centralMovingAverage
- ignoreMissings: when true, missing values are ignored and each average will be calculated from the available values within the current time window.
When false, calculated values will be set to missing if one or more values within the current time window are missing. - movingAccumulationTimeSpan: Time spans that the user selects from using the slider.
The central moving average function only works for true equidistant data (i.e. no daysOfMonths etc.)
The difference between moving average and central moving average is that the central moving average uses values before and after the current value to calculate the average. Moving average only uses values in the past.
accumulation interval (with associated time span or time step)
...
The accumulation interval sums the values for all time steps within the selected time window range. The time window range is defined by the associated time span or time step.
...
- function: accumulationInterval
- movingAccumulationTimeSpan: Time spans that the user selects from using the slider.
or - timeStep: Time steps that the user selects from using the slider.
frequency distribution (with associated samples)
...
The data range of the y-axis is determined by the minimum value to the maximum value of the timeseries. The frequency of the available values are counted and plotted within the range intervals. The selected sample determines in how many range intervals the data range should be divided into and therefor the width of the range intervals.
...
- function: frequencyDistribution
- samples: The number Definition of intervals that the user selects from using the slider.
gaussian curve (with associated samples)
...
The mean value and standard deviation are calculated for the timeseries from which the normal distrubution function is calculated. The selected sample determines in how many samples the normal distribution function is divided into.
Attributes:
- function: gaussianCurve.
- ignoreMissings: when true, missing values are ignored and each average will be calculated from the available values within the current time window.
When false, calculated values will be set to missing if one or more values within the current time window are missing. - samples: The number Definition of samples sizes that the user selects from using the slider.
...