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Uncertain index-value dataset

UncertainIndexValueDatasets have uncertainties associated with both the indices (e.g. time, depth, etc) and the values of the data points.

# UncertainData.UncertainDatasets.UncertainIndexValueDataset — Type.

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UncertainIndexValueDataset

A generic dataset type consisting of uncertain values whose indices (time, depth, order, etc...) are also uncertain value.

Fields

  • values::T where {T <: AbstractUncertainValueDataset}: The uncertain indices. Will in general be an UncertainIndexDataset, but does not necessarily have to be. Each index is represented by an AbstractUncertainValue.
  • values::UncertainDataset: The uncertain values. Each value is represented by an AbstractUncertainValue.

source

Example

Here's an example of how to create an uncertain index-value dataset. Let's start by defining the uncertain data values and collecting them in an UncertainValueDataset. 

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using UncertainData, Plots 
gr()
r1 = [UncertainValue(Normal, rand(), rand()) for i = 1:10]
r2 = UncertainValue(rand(10000))
r3 = UncertainValue(Uniform, rand(10000))
r4 = UncertainValue(Normal, -0.1, 0.5)
r5 = UncertainValue(Gamma, 0.4, 0.8)

u_values = [r1; r2; r3; r4; r5]
udata = UncertainDataset(u_values);

The values were measures at some time indices by an inaccurate clock, so that the times of measuring are normally distributed values with fluctuating standard deviations.

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u_timeindices = [UncertainValue(Normal, i, rand(Uniform(0, 1))) 
    for i = 1:length(udata)]
uindices = UncertainDataset(u_timeindices);

Now, combine the uncertain time indices and measurements into an UncertainIndexValueDataset.

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x = UncertainIndexValueDataset(uindices, udata)

The built-in plot recipes make it easy to visualize the dataset. By default, plotting the dataset plots the median value of the index and the measurement (only for scatter plots), along with the 33rd to 67th percentile range error bars in both directions. 

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plot(x)

You can also tune the error bars by calling plot(udata::UncertainIndexValueDataset, idx_quantiles, val_quantiles), explicitly specifying the quantiles in each direction, like so:

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plot(x, [0.05, 0.95], [0.05, 0.95])