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Changelog

Changelog

UncertainData.jl v0.10.3

Improvements

  • The user can now control how each bin is represented when using BinnedWeightedResampling. One can now provide BinnedWeightedResampling{UncertainScalarKDE}, BinnedWeightedResampling{UncertainScalarPopulaton} or BinnedWeightedResampling{RawValues}. Corresponding bin methods are also implemented.

Documentation

  • Fixed missing doc string for bin!.

UncertainData.jl v0.10.2

Improvements

  • The user can now control how each bin is represented when using BinnedResampling. One can now provide BinnedResampling{UncertainScalarKDE}, BinnedResampling{UncertainScalarPopulaton} or BinnedResampling{RawValues}.
  • Explicit bin methods for binning both scalar valued data and uncertain data.

Documentation

  • Added documentation for binning methods.
  • Improved documentation for UncertainScalarKDE.

UncertainData.jl v0.10.0

Improvements

  • The resample family of methods for vectors now dispatches on AbstractVectors, which allows more flexibility. Now, for example LArrays from LabelledArrays.jl also can be resampled.
  • Relax resample(x::Real) to resample(x::Number).

UncertainData.jl v0.9.3

  • dimension is no longer exported.

UncertainData.jl v0.9.2

New features

  • Added SensitivityTests module defining the abstract type SensitivityTest.

UncertainData.jl v0.9.1

Bug fixes

  • Missing import of interpolate_and_bin between sub-packages fixed.

Uncertaindata.jl v0.9.0

New features

  • Added interpolate_and_bin function.
  • Added InterpolateAndBin type.
  • Added resample(inds, vals, resampling::InterpolateAndBin{Linear}) method, which interpolates and bins inds and vals onto an interpolation grid, then bins and summarises the bins. Returns the binned values.
  • Added resample(x::AbstractUncertainIndexValueDataset, resampling::InterpolateAndBin{Linear}) method. Draws a single realisation of both the indices and values of x and orders them sequentially according to the indices (assuming independent points). Then, interpolate, bin and summarise bins.
  • Added bin and bin! functions.
  • Added bin_mean function.
  • Added fill_nans, fill_nans! and interpolate_nans functions for dealing with data containing NaNs.
  • Added findall_nan_chunks function for identifying consecutive NaNs in a dataset.
  • Added RandomSequences resampling scheme.

Uncertaindata.jl v0.8.2

New features

  • Added resample method for BinnedWeightedResampling scheme.
  • Added AbstractBinnedResampling for binned resamplings.
  • Added AbstractBinnedUncertainValueResampling abstract type for binnings where the values in each bin is represented by an uncertain value. BinnedResampling and BinnedWeightedResampling are subtypes AbstractBinnedUncertainValueResampling.
  • Added AbstractBinnedSummarisedResampling abstract type for binnings where the values in each bin are summarised to a single value. BinnedMeanResampling and BinnedMeanWeightedResampling are subtypes AbstractBinnedResampling.

Improvements

  • Added more tests for binned resampling schemes.

Uncertaindata.jl v0.8.1

New features

  • Added UncertainValueDataset, UncertainIndexDataset, and UncertainIndexValueDataset constructors for vectors of numbers (they get converted to CertainValues).

Bug fixes

  • rand(x::CertainValue, n::Int) now returns a length-n array with x repeated n times.

Uncertaindata.jl v0.8.0

New functionality

Bug fixes

  • Fixed bug where resample! method for vectors and tuples of uncertain values didn't return the expected result.

Improvements

  • Improved resample! docs.

Uncertaindata.jl v0.7.0

New functionality

  • Added resample! for in-place resampling into pre-allocated containers.

UncertainData.jl v0.5.1

Bug fixes

  • Strictly increasing or decreasing sequences were not always possible to construct when using CertainValues, because TruncateRange instances with equal minimum and maximum was constructed (not possible). It is now possible to resample with sequential constraints even with the StrictlyIncreasing and StrictlyDecreasing constraints.

UncertainData.jl v0.5.0

Breaking changes

  • To allow easier multiple dispatch, the indices field of a UncertainIndexValueDataset is now always an instance of a subtype of AbstractUncertainIndexDataset. The values field of a UncertainIndexValueDataset is now always an instance of a subtype of AbstractUncertainValueDataset.

New functionality

  • Experimental support for nested populations.

  • Added point-estimators for single uncertain values:

    1. harmmean(x::AbstractUncertainValue, n::Int)
    2. geomean(x::AbstractUncertainValue, n::Int)
    3. kurtosis(x::AbstractUncertainValue, n::Int; m = mean(x))
    4. moment(x::AbstractUncertainValue, k, n::Int, m = mean(x))
    5. percentile(x::AbstractUncertainValue, p, n::Int)
    6. renyientropy(x::AbstractUncertainValue, α, n::Int)
    7. rle(x::AbstractUncertainValue, n::Int)
    8. sem(x::AbstractUncertainValue, n::Int)
    9. skewness(x::AbstractUncertainValue, n::Int; m = mean(x))
    10. span(x::AbstractUncertainValue, n::Int)
    11. summarystats(x::AbstractUncertainValue, n::Int)
    12. totalvar(x::AbstractUncertainValue, n::Int)

    13. Added statistical estimators for pairs of uncertain values:

    14. cov(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int; corrected::Bool = true)

    15. cor(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    16. countne(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    17. counteq(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    18. corkendall(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    19. corspearman(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    20. maxad(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    21. meanad(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    22. msd(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    23. psnr(x::AbstractUncertainValue, y::AbstractUncertainValue, maxv, n::Int)
    24. rmsd(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int; normalize = false)
    25. sqL2dist(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    26. crosscor(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int; demean = true)
    27. crosscov(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int; demean = true)
    28. gkldiv(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    29. kldivergence(x::AbstractUncertainValue, y::AbstractUncertainValue, n::Int)
    30. Added UncertainValue constructor for distribution instances.
    31. Added UncertainValue constructor for (potentially nested) truncated distribution instances.
    32. Implemented resample methods for NTuples of uncertain values.
    33. Added resample(f::Function, n::Int, x::AbstractUncertainValue, args...; kwargs...)method for easy evaluation of point-estimates for single uncertain values.
    34. Added support for Measurement instances from Measurements.jl. These are treated as uncertain values represented by normal distibutions. Hence, they are given no extra treatment and error propagation is done by resampling, not by exact methods.
    35. The uncertain value type UncertainScalarPopulation may now not only have real-valued scalars as elements of the population. It can now have uncertain values as members of the population!
    36. Resampling implemented for UncertainScalarPopulation so that we can also sample population members that are uncertain values.
    37. Implemented iteration for UncertainScalarPopulation.

Improvements

  • Improved subtyping for theoretical distributions.
  • Removed redundant resample methods for the UncertainDataset type. UncertainDataset is a subtype of AbstractUncertainValueDataset and has no special behaviour beyond that implemented for the abstract type, so now we just rely on multiple dispatch here.

Documentation

  • Improved documentation statistical methods.
  • Other minor documentation improvements.
  • Improved documentation for TruncateStd.

Bug fixes

  • Fixed error in show method for AbstractUncertainValue. Not subtypes of AbstractUncertainValue has the distributions field, so that is now removed from the show method.

UncertainData.jl v0.4.0

New functionality

  • Introduce an abstract resampling type AbstractUncertainDataResampling for this package pending the implementation of AbstractResampling in StatsBase.jl.
  • Added ConstrainedResampling resampling scheme.

  • Resample vectors of uncertain values without constraints. Syntax:

    1. resample(::Vector{<:AbstractUncertainValue} for single draws.
    2. resample(::Vector{<:AbstractUncertainValue}, ::Int} for multiple draws.

    3. Resample vectors of uncertain values with constraint(s) multiple times. Syntax:

    4. resample(::Vector{<:AbstractUncertainValue}, ::Union{SamplingConstraint, Vector{<:SamplingConstraint}} for single draws.

    5. resample(::Vector{<:AbstractUncertainValue}, ::Union{SamplingConstraint, Vector{<:SamplingConstraint}}, ::Int for multiple draws.

UncertainData.jl v0.3.0

New functionality

  • Added additional resampling methods for uncertain index and uncertain value datasets, allowing passing vectors of constraints that are mapped to each value in the dataset. The syntax is resample(::AbstractUncertainValueDataset, ::Vector{<:SamplingConstraint} for a single draw, and resample(::AbstractUncertainValueDataset, ::Vector{<:SamplingConstraint}, n::Int for n draws.

UncertainData.jl v0.2.3

Improvements

  • Added input validation when initialising TruncateQuantiles, TruncateRange and TruncateStd.
  • Separate parameters types for TruncateQuantiles and TruncateRange, so one can do for example TruncateRange(1, 8.0), instead of having to promote to Float64.
  • Added validation for distribution truncation when resampling.

UncertainData.jl v0.2.2

New functionality and syntax changes

Resampling vectors consisting of uncertain values (done in #61)
  • resample(uvals::Vector{AbstractUncertainValue}, n::Int) is now interpreted as "treat uvals as a dataset and sample it n times". Thus, it now behaves as resample(AbstractUncertainDataset, n::Int), returning n vectors of length length(uvals), where the i-th element is a unique draw of uvals[i].
  • resample_elwise(uvals::Vector{AbstractUncertainValue}, n::Int) takes over the role as "sample uvals element-wise and n times for each element". Returns a vector of length length(uvals), where the i-th element is a n-element vector of unique draws of uvals[i].

Resampling with subtypes of AbstractUncertainValueDataset

Currently, this affects the generic UncertainDatasets, as well as the specialized UncertainIndexDatasets and UncertainValueDatasets.

  • resample_elwise(uvd::AbstractUncertainValueDataset, n::Int) is now interpreted as "draw n realisations of each value in uvd". Returns a vector of length length(uvals) where the i-th element is a n-element vector of unique draws of uvals[i]. This works for UncertainDatasets, UncertainIndexDatasets, and UncertainValueDatasets.
  • resample_elwise(uvd::AbstractUncertainValueDataset, constraint::Union{SamplingConstraint, Vector{SamplingConstraint}}, n::Int) is now interpreted as "draw n realisations of each value in uvd, subjecting each value in uvd to some sampling constraint(s) during resampling". Returns a vector of length length(uvals) where the i-th element is a n-element vector of unique draws of uvals[i], where the support of uvals[i] has been truncated by the provided constraint(s).

Bug fixes

  • Removed extra blank line from print method for AbstractUncertainPopulation.

UncertainData.jl v0.2.1

New functionality

  • merge(uvals::Vector{<:AbstractUncertainValue}; n = 1000) now makes it possible to combine many uncertain values of different into one uncertain value represented by a kernel density estimate. This is achieved by resampling each of the values n times, then pooling the draws and estimating a total distribution using KDE.
  • merge(uvals::Vector{<:AbstractUncertainValue}; weights::Weights n = 1000), merge(uvals::Vector{<:AbstractUncertainValue}; weights::AnalyticalWeights n = 1000) and merge(uvals::Vector{<:AbstractUncertainValue}; weights::ProbabilityWeights n = 1000) merges uncertain values by resampling them proportionally to weights, then pooling the draws and performing KDE. These are all functionally equivalent, but implementations for different weights are provided for compatibility with StatsBase.
  • merge(uvals::Vector{<:AbstractUncertainValue}; weights::FrequencyWeights n = 1000) merges uncertain values by sampling them according to the number of samples provided with weights.

Bug fixes

  • resample didn't work for UncertainIndexDatasets due to the data being stored in the indices field, not the values field as for other subtypes of AbstractUncertainValueDataset. This is now fixed.

UncertainData.jl v0.2.0

Notes

  • Julia 1.1 is required for version > v.0.2.0.

New functionality

  • Spline interpolation on a regular grid.
  • Linear interpolation on an irregular grid.

Improvements

  • support_overlap now returns an interval (from IntervalArithmetic), in line with what support returns.

UncertainData.jl v0.1.8

Bug fixes

  • Added missing package dependencies which were not caught by CI.

UncertainData.jl v0.1.7

New functionality

  • UncertainIndexValueDatasets can now be constructed from vectors of uncertain values. To do so, provide a vector of uncertain values for the indices, and the same for the values, e.g. UncertainIndexValueDataset([idx1, idx2], [val1, val2]).
  • Index-value dataset realizations can now be interpolated on a regular grid.

Bug fixes

  • minima and maxima now returns the global minimum for a dataset instead of a vector of elementwise minima and maxima.
  • Implemented the option to linearly interpolate index-value dataset realizations. To do so, provide resample with a RegularGrid instance.
  • Merged redundant methods for assigning some distributions.
  • Fixed non-critical indexing bug for uncertain index-value datasets.
  • Removed redudant method definitions and multiple imports of the same files causing definitions to be overwritten and printing warnings statements when loading the package.

UncertainData.jl v0.1.6

New functionality

  • Implemented sequential sampling constraints StrictlyIncreasing and StrictlyDecreasing for UncertainIndexValueDatasets.
  • Added UncertainScalarPopulation type, representing vectors of values that should be sampled according to a vector of probabilities.

Improvements

  • Improved documentation for CertainValues.
  • Added documentation for UncertainScalarPopulation.
  • Added UncertainScalarPopulation to uncertain value overview list in the documentation.
  • Fixed duplicate docs for cot, cotd, coth and added missing acot, acotd, acoth docs.
  • Shortened and updated main documentation page with more links.

Bug fixes

  • Import Base functions properly when defining CertainValue, so that no unexpected behaviour is introduced.
  • Fixed links in documentation that pointed to the wrong locations.
  • Remove model resampling docs which was not supposed to be published until the functionality is properly implemented.

UncertainData.jl v0.1.5

New functionality

  • Added CertainValue type to represent scalars without any uncertainty. Even though a scalar is not uncertain, we'll define it as subtype of AbstractUncertainValue to treat certain values alongside uncertain values in datasets.
  • Added plot recipe for CertainValues. They are just plotted as regular points.
  • Added method resample(Vector{AbstractUncertainValue}) for resampling vectors of uncertain values. Operates element-wise, just as for an uncertain dataset.
  • Added an abstract type SequentialSamplingConstraint to separate sequential constraints from general constraints that might be applied before resampling according to the sequential constraints.
  • Added abstract type (OrderedSamplingAlgorithm) and composite types (StartToEnd, EndToStart, MidpointOutwards, ChunksForwards, ChunksBackwards) which indicates how to sample sequential realizations when resampling an uncertain dataset. Only StartToEnd is used at the moment.
  • Added abstract type SequentialSamplingConstraint which is the supertype for all sequential constraints.
  • Added function to check if strictly increasing sequences through an uncertain dataset exist: strictly_increasing_sequence_exists(udata::AbstractUncertainValueDataset.
  • Added function to check if strictly decreasing sequences through an uncertain dataset exist: strictly_increasing_sequence_exists(udata::AbstractUncertainValueDataset.
  • Added the StrictlyIncreasing{T} where {T<:OrderedSamplingAlgorithm} sequential constraint for resampling uncertain datasets.
  • Added the StrictlyDecreasing{T} where {T<:OrderedSamplingAlgorithm} sequential constraint for resampling uncertain datasets.

  • Added resampling methods

    1. resample(udata, sequential_constraint::StrictlyIncreasing{T} where {T <: StartToEnd}
    2. resample(udata, sequential_constraint::StrictlyDecreasing{T} where {T <: StartToEnd}
    3. resample(udata, constraint::SamplingConstraint, sequential_constraint::StrictlyIncreasing{T} where {T <: StartToEnd}
    4. resample(udata, constraint::SamplingConstraint, sequential_constraint::StrictlyDecreasing{T} where {T <: StartToEnd}
    5. resample(udata, constraint::Vector{SamplingConstraint}, sequential_constraint::StrictlyIncreasing{T} where {T <: StartToEnd}
    6. resample(udata, constraint::Vector{SamplingConstraint}, sequential_constraint::StrictlyDecreasing{T} where {T <: StartToEnd}

Improvements

UncertainData.jl v0.1.4

Breaking changes

  • Elementary operations for (scalar, uncertain_value), (uncertain_value, scalar) and (uncertain_value, uncertain_value) pairs now returns an uncertain value instead of a vector of resampled realizations. The default behaviour is to perform a kernel density estimate over the vector of results of the element-wise operations (which was previously returned without representing it as an uncertain value).

New functionality

  • Implemented constraints for datasets that have already been constrained. constrain(udata::ConstrainedDataset, s::SamplingConstraint) will now return another ConstrainedDataset. The same applies for ConstrainedIndexDataset and ConstrainedValueDataset.
  • Added maximum(Vector{AbstractUncertainValue}) and minimum(Vector{AbstractUncertainValue}) methods.
  • Added plot recipe for Vector{AbstractUncertainValue}s. Behaves just as plotting an uncertain dataset, assuming an implicit indices 1:length(v). Error bars may be tuned by providing a second argument of quantiles to plot, e.g. plot(v, [0.2, 0.8] gives error bars covering the 20th to 80th percentile range of the data.

Improvements

  • Added documentation for StrictlyIncreasing and StrictlyDecreasing sampling constraints.
  • Added show function for AbstractUncertainIndexDataset. show errored previously, because it assumed the default behaviour of AbstractUncertainValueDataset, which does not have the indices field.

Bug fixes

  • Fixed bug when resampling an uncertain dataset using the NoConstraint constraint, which did not work to due to a reference to a non-existing variable.
  • Fixed test bug where when resampling an uncertain value with the TruncateStd sampling constraint, the test compared the result to a fixed scalar, not the standar deviation of the value. This sometimes made the travis build fail.

UncertainData.jl v0.1.3

New functionality

  • Allow both the indices and values fields of UncertainIndexValueDataset to be any subtype of AbstractUncertainValueDataset. This way, you don't have to use an index dataset type for the indices if not necessary.

Improvements

UncertainData.jl v0.1.2

New functionality

  • Support elementary mathematical operations (+, -, * and /) between arbitrary uncertain values of different types. Also works with the combination of scalars and uncertain values. Because elementary operations should work on arbitrary uncertain values, a resampling approach is used to perform the mathematical operations. This means that all mathematical operations return a vector containing the results of repeated element-wise operations (where each element is a resampled draw from the furnishing distribution(s) of the uncertain value(s)). The default number of realizations is set to 10000. This allows calling uval1 + uval2 for two uncertain values uval1 and uval2. If you need to tune the number of resample draws to n, you need to use the +(uval1, uval2, n) syntax (similar for the operators). In the future, elementary operations might be improved for certain combinations of uncertain values where exact expressions for error propagation are now, for example using the machinery in Measurements.jl for normally distributed values.
  • Support for trigonometric functions added (sin, sind, sinh, cos, cosd, cosh, tan, tand, tanh, csc, cscd, csch, csc, cscd, csch, sec, secd, sech, cot, cotd, coth, sincos, sinc, sinpi, cosc, cospi). Inverses are also defined (asin, asind, asinh, acos, acosd, acosh, atan, atand, atanh, acsc, acscd, acsch, acsc, acscd, acsch, asec, asecd, asech, acot, acotd, acoth). Beware: if the support of the funishing distribution for an uncertain value lies partly outside the domain of the function, you risk encountering errors. These also use a resampling approach, using 10000 realizations by default. Use either the sin(uval) syntax for the default, and sin(uval, n::Int) to tune the number of samples.
  • Support non-integer multiples of the standard deviation in the TruncateStd sampling constraint.

Fixes

  • Fixed bug in resampling of index-value datasets, where the n arguments wasn't used.
  • Bugfix: due to StatsBase.std not being defined for FittedDistribution instances, uncertain values represented by UncertainScalarTheoreticalFit instances were not compatible with the TruncateStd sampling constraint. Now fixed!
  • Added missing resample(uv::AbstractUncertainValue, constraint::TruncateRange, n::Int) method.

Improvements

  • Improved resampling documentation for UncertainIndexValueDatasets. Now shows the documentation for the main methods, as well as examples of how to use different sampling constraints for each individual index and data value.
  • Improved resampling documentation for UncertainDatasets. Now shows the documentation for the main methods.

UncertainData.jl v0.1.1

New functionality

  • Indexing implemented for UncertainIndexValueDataset.
  • Resampling implemented for UncertainIndexValueDataset.
  • Uncertain values and uncertain datasets now support minimum and maximum.
  • support(uv::AbstractUncertainValue) now always returns an interval from IntervalArithmetic.jl
  • support_overlap now computes overlaps also for fitted theoretical distributions.
  • Added more plotting recipes.
  • All implemented uncertain data types now support resampling.

Improvements

  • Improved general documentation. Added a reference to Measurements.jl and an explanation for the differences between the packages.
  • Improved resampling documentation with detailed explanation and plots.

UncertainData.jl v0.1.0

  • Basic functionality in place.