# Release notes¶

## v1.0.0¶

This release includes some subtle but important changes to the architecture of
the data structures modules (`allel.model.ndarray`

,
`allel.model.chunked`

, `allel.model.dask`

). These changes are mostly
backwards-compatible but in some cases could break existing code, hence the
major version number has been incremented. Also included in this release are
some new functions related to Mendelian inheritance and calling runs of
homozygosity, further details below.

### Mendelian errors and phasing by transmission¶

This release includes a new `allel.stats.mendel`

module with functions to
help with analysis of related individuals. The function
`allel.stats.mendel.mendel_errors()`

locates genotype calls within a trio or
cross that are not consistent with Mendelian segregation of alleles. The
function `allel.stats.mendel.phase_by_transmission()`

will resolve unphased
diploid genotypes into phased haplotypes for a trio or cross using Mendelian
transmission rules. The function `allel.stats.mendel.paint_transmission()`

can help with evaluating and visualizing the results of phasing a trio or cross.

### Runs of homozygosity¶

A new `allel.stats.roh.roh_mhmm()`

function provides support for locating
long runs of homozygosity within a single sample. The function uses a
multinomial hidden Markov model to predict runs of homozygosity based on the
rate of heterozygosity over the genome. The function can also incorporate
information about which positions in the genome are not accessible to variant
calling and hence where there is no information about heterozygosity, to reduce
false calling of ROH in regions where there is patchy data. We’ve run this on
data from the Ag1000G project but have not performed a comprehensive evaluation
with other species, feedback is very welcome.

### Changes to data structures¶

The `allel.model.ndarray`

module includes a new
`allel.model.ndarray.GenotypeVector`

class. This class represents an
array of genotype calls for a single variant in multiple samples, or for a
single sample at multiple variants. This class makes it easier, for example, to
locate all variants which are heterozygous in a single sample.

Also in the same module are two new classes
`allel.model.ndarray.GenotypeAlleleCountsArray`

and
`allel.model.ndarray.GenotypeAlleleCountsVector`

. These classes provide
support for an alternative encoding of genotype calls, where each call is stored
as the counts of each allele observed. This allows encoding of genotype calls
where samples may have different ploidy for a given chromosome (e.g.,
*Leishmania*) and/or where samples carry structural variation within some genome
regions, altering copy number (and hence effective ploidy) with respect to the
reference sequence.

There have also been architectural changes to all data structures modules. The
most important change is that all classes in the `allel.model.ndarray`

module now **wrap** numpy arrays and are no longer direct sub-classes of the
numpy `numpy.ndarray`

class. These classes still **behave** like numpy
arrays in most respects, and so in most cases this change should not impact
existing code. If you need a plain numpy array for any reason you can always use
`numpy.asarray()`

or access the `.values`

property, e.g.:

```
>>> import allel
>>> import numpy as np
>>> g = allel.GenotypeArray([[[0, 1], [0, 0]], [[0, 2], [1, 1]]])
>>> isinstance(g, np.ndarray)
False
>>> a = np.asarray(g)
>>> isinstance(a, np.ndarray)
True
>>> isinstance(g.values, np.ndarray)
True
```

This change was made because there are a number of complexities that arise when sub-classing class:numpy.ndarray and these were proving tricky to manage and maintain.

The `allel.model.chunked`

and `allel.model.dask`

modules also follow
the same wrapper pattern. For the `allel.model.dask`

module this means a
change in the way that classes are instantiated. For example, to create a
`allel.model.dask.GenotypeDaskArray`

, pass the underlying data directly
into the class constructor, e.g.:

```
>>> import allel
>>> import h5py
>>> h5f = h5py.File('callset.h5', mode='r')
>>> h5d = h5f['3R/calldata/genotype']
>>> genotypes = allel.GenotypeDaskArray(h5d)
```

If the underlying data is chunked then there is no need to specify the chunks manually when instantiating a dask array, the native chunk shape will be used.

Finally, the allel.model.bcolz module has been removed, use either
the `allel.model.chunked`

or `allel.model.dask`

module
instead.

## v0.21.2¶

This release resolves compatibility issues with Zarr version 2.1.

## v0.21.1¶

- Added parameter min_maf to
`allel.stats.selection.ihs()`

to skip IHS calculation for variants below a given minor allele frequency. - Minor change to calculation of integrated haplotype homozygosity to enable values to be reported for first and last variants if include_edges is True.
- Minor change to
`allel.stats.selection.standardize_by_allele_count()`

to better handle missing values.

## v0.21.0¶

In this release the implementations of `allel.stats.selection.ihs()`

and `allel.stats.selection.xpehh()`

selection statistics have been
reworked to address a number of issues:

- Both functions can now integrate over either a genetic map (via the map_pos parameter) or a physical map.
- Both functions now accept max_gap and gap_scale parameters to perform adjustments to integrated haplotype homozygosity where there are large gaps between variants, following the standard approach. Alternatively, if a map of genome accessibility is available, it may be provided via the is_accessible parameter, in which case the distance between variants will be scaled by the fraction of accessible bases between them.
- Both functions are now faster and can make use of multiple threads to further accelerate computation.
- Several bugs in the previous implementations of these functions have been fixed (#91).
- New utility functions are provided for standardising selection scores,
see
`allel.stats.selection.standardize_by_allele_count()`

(for use with IHS and NSL) and`allel.stats.selection.standardize()`

(for use with XPEHH).

Other changes:

- Added functions
`allel.stats.diversity.moving_tajima_d()`

and`allel.stats.selection.moving_delta_tajima_d()`

(#81, #70). - Added functions
`allel.stats.fst.moving_weir_cockerham_fst()`

,`allel.stats.fst.moving_hudson_fst()`

,`allel.stats.fst.moving_patterson_fst()`

. - Added functions
`allel.stats.admixture.moving_patterson_f3()`

and`allel.stats.admixture.moving_patterson_d()`

. - Renamed “blockwise” to “average” in function names in
`allel.stats.fst`

and`allel.stats.admixture`

for clarity. - Added convenience methods
`allel.model.ndarray.AlleleCountsArray.is_biallelic()`

and`allel.model.ndarray.AlleleCountsArray.is_biallelic_01()`

for locating biallelic variants. - Added support for zarr in the
`allel.chunked`

module (#101). - Changed HDF5 default chunked storage to use gzip level 1 compression instead of no compression (#100).
- Fixed bug in
`allel.stats.diversity.sequence_divergence()`

(#75). - Added workaround for chunked arrays if passed as arguments into numpy aggregation functions (#66).
- Protect against invalid coordinates when mapping from square to condensed coords (#83).
- Fixed bug in
`allel.stats.sf.plot_sfs_folded()`

and added docstrings for all plotting functions in`allel.stats.sf`

(#80). - Fixed bug related to taking views of genotype and haplotype arrays (#77).

## v0.20.3¶

- Fixed a bug in the count_alleles() methods on genotype and haplotype array classes that manifested if the max_allele argument was provided (#59).
- Fixed a bug in Jupyter notebook display method for chunked tables (#57).
- Fixed a bug in site frequency spectrum scaling functions (#54).
- Changed behaviour of subset method on genotype and haplotype arrays to better infer argument types and handle None argument values (#55).
- Changed table eval and query methods to make python the default for expression evaluation, because it is more expressive than numexpr (#58).

## v0.20.2¶

## v0.20.1¶

- Changed functions
`allel.stats.fst.weir_cockerham_fst()`

and`allel.stats.ld.locate_unlinked()`

such that chunked implementations are now used by default, to avoid accidentally and unnecessarily loading very large arrays into memory (#50).

## v0.20.0¶

- Added new
`allel.model.dask`

module, providing implementations of the genotype, haplotype and allele counts classes backed by dask.array (#32). - Released the GIL where possible in Cython optimised functions (#43).
- Changed functions in
`allel.stats.selection`

that accept min_ehh argument, such that min_ehh = None should now be used to indicate that no minimum EHH threshold should be applied.

## v0.19.0¶

The major change in v0.19.0 is the addition of the new
`allel.model.chunked`

module, which provides classes for variant
call data backed by chunked array storage (#31). This is a
generalisation of the previously available `allel.model.bcolz`

to
enable the use of both bcolz and HDF5 (via h5py) as backing
storage. The `allel.model.bcolz`

module is now deprecated but
will be retained for backwargs compatibility until the next major
release.

Other changes:

- Added function for computing the number of segregating sites by length
(nSl), a summary statistic comparing haplotype homozygosity between
different alleles (similar to IHS), see
`allel.stats.selection.nsl()`

(#40). - Added functions for computing haplotype diversity, see
`allel.stats.selection.haplotype_diversity()`

and`allel.stats.selection.moving_haplotype_diversity()`

(#29). - Added function
`allel.stats.selection.plot_moving_haplotype_frequencies()`

for visualising haplotype frequency spectra in moving windows over the genome (#30). - Added vstack() and hstack() methods to genotype and haplotype arrays to enable combining data from multiple arrays (#21).
- Added convenience function
`allel.stats.window.equally_accessible_windows()`

(#16). - Added methods from_hdf5_group() and to_hdf5_group() to
`allel.model.ndarray.VariantTable`

(#26). - Added
`allel.util.hdf5_cache()`

utility function. - Modified functions in the
`allel.stats.selection`

module that depend on calculation of integrated haplotype homozygosity to return NaN when haplotypes do not decay below a specified threshold (#39). - Fixed missing return value in
`allel.stats.selection.plot_voight_painting()`

(#23). - Fixed return type from array reshape() (#34).

## v0.18.1¶

- Minor change to the Garud H statistics to avoid raising an exception when the number of distinct haplotypes is very low (#20).

## v0.18.0¶

- Added functions for computing H statistics for detecting signatures of soft
sweeps, see
`allel.stats.selection.garud_h()`

,`allel.stats.selection.moving_garud_h()`

,`allel.stats.selection.plot_haplotype_frequencies()`

(#19). - Added function
`allel.stats.selection.fig_voight_painting()`

to paint both flanks either side of some variant under selection in a single figure (#17). - Changed return values from
`allel.stats.selection.voight_painting()`

to also return the indices used for sorting haplotypes by prefix (#18).

## v0.17.0¶

- Added new module for computing and plotting site frequency spectra, see
`allel.stats.sf`

(#12). - All plotting functions have been moved into the appropriate stats module
that they naturally correspond to. The
`allel.plot`

module is deprecated (#13). - Improved performance of carray and ctable loading from HDF5 with a condition (#11).

## v0.16.2¶

## v0.16.1¶

- Added block-wise implementation to
`allel.stats.ld.locate_unlinked()`

so it can be used with compressed arrays as input.

## v0.16.0¶

- Added new selection module with functions for haplotype-based analyses of
recent selection, see
`allel.stats.selection`

.

## v0.15.2¶

- Improved performance of
`allel.model.bcolz.carray_block_compress()`

,`allel.model.bcolz.ctable_block_compress()`

and`allel.model.bcolz.carray_block_subset()`

for very sparse selections. - Fix bug in IPython HTML table captions.
- Fix bug in addcol() method on bcolz ctable wrappers.

## v0.15.1¶

- Fix missing package in setup.py.

## v0.15¶

- Added functions to estimate Fst with standard error via a
block-jackknife:
`allel.stats.fst.blockwise_weir_cockerham_fst()`

,`allel.stats.fst.blockwise_hudson_fst()`

,`allel.stats.fst.blockwise_patterson_fst()`

. - Fixed a serious bug in
`allel.stats.fst.weir_cockerham_fst()`

related to incorrect estimation of heterozygosity, which manifested if the subpopulations being compared were not a partition of the total population (i.e., there were one or more samples in the genotype array that were not included in the subpopulations to compare). - Added method
`allel.model.AlleleCountsArray.max_allele()`

to determine highest allele index for each variant. - Changed first return value from admixture functions
`allel.stats.admixture.blockwise_patterson_f3()`

and`allel.stats.admixture.blockwise_patterson_d()`

to return the estimator from the whole dataset. - Added utility functions to the
`allel.stats.distance`

module for transforming coordinates between condensed and uncondensed forms of a distance matrix. - Classes previously available from the allel.model and
allel.bcolz modules are now aliased from the root
`allel`

module for convenience. These modules have been reorganised into an`allel.model`

package with sub-modules`allel.model.ndarray`

and`allel.model.bcolz`

. - All functions in the
`allel.model.bcolz`

module use cparams from input carray as default for output carray (convenient if you, e.g., want to use zlib level 1 throughout). - All classes in the
`allel.model.ndarray`

and`allel.model.bcolz`

modules have changed the default value for the copy keyword argument to False. This means that**not**copying the input data, just wrapping it, is now the default behaviour. - Fixed bug in
`GenotypeArray.to_gt()`

where maximum allele index is zero.

## v0.14¶

- Added a new module
`allel.stats.admixture`

with statistical tests for admixture between populations, implementing the f2, f3 and D statistics from Patterson (2012). Functions include`allel.stats.admixture.blockwise_patterson_f3()`

and`allel.stats.admixture.blockwise_patterson_d()`

which compute the f3 and D statistics respectively in blocks of a given number of variants and perform a block-jackknife to estimate the standard error.

## v0.12¶

- Added functions for principal components analysis of genotype
data. Functions in the new module
`allel.stats.decomposition`

include`allel.stats.decomposition.pca()`

to perform a PCA via full singular value decomposition, and`allel.stats.decomposition.randomized_pca()`

which uses an approximate truncated singular value decomposition to speed up computation. In tests with real data the randomized PCA is around 5 times faster and uses half as much memory as the conventional PCA, producing highly similar results. - Added function
`allel.stats.distance.pcoa()`

for principal coordinate analysis (a.k.a. classical multi-dimensional scaling) of a distance matrix. - Added new utility module
`allel.stats.preprocessing`

with classes for scaling genotype data prior to use as input for PCA or PCoA. By default the scaling (i.e., normalization) of Patterson (2006) is used with principal components analysis functions in the`allel.stats.decomposition`

module. Scaling functions can improve the ability to resolve population structure via PCA or PCoA. - Added method
`allel.model.GenotypeArray.to_n_ref()`

. Also added`dtype`

argument to`allel.model.GenotypeArray.to_n_ref()`

and`allel.model.GenotypeArray.to_n_alt()`

methods to enable direct output as float arrays, which can be convenient if these arrays are then going to be scaled for use in PCA or PCoA. - Added
`allel.model.GenotypeArray.mask`

property which can be set with a Boolean mask to filter genotype calls from genotype and allele counting operations. A similar property is available on the`allel.bcolz.GenotypeCArray`

class. Also added method`allel.model.GenotypeArray.fill_masked()`

and similar method on the`allel.bcolz.GenotypeCArray`

class to fill masked genotype calls with a value (e.g., -1).

## v0.11¶

- Added functions for calculating Watterson’s theta (proportional to
the number of segregating variants):
`allel.stats.diversity.watterson_theta()`

for calculating over a given region, and`allel.stats.diversity.windowed_watterson_theta()`

for calculating in windows over a chromosome/contig. - Added functions for calculating Tajima’s D statistic (balance
between nucleotide diversity and number of segregating sites):
`allel.stats.diversity.tajima_d()`

for calculating over a given region and`allel.stats.diversity.windowed_tajima_d()`

for calculating in windows over a chromosome/contig. - Added
`allel.stats.diversity.windowed_df()`

for calculating the rate of fixed differences between two populations. - Added function
`allel.model.locate_fixed_differences()`

for locating variants that are fixed for different alleles in two different populations. - Added function
`allel.model.locate_private_alleles()`

for locating alleles and variants that are private to a single population.

## v0.10¶

- Added functions implementing the Weir and Cockerham (1984)
estimators for F-statistics:
`allel.stats.fst.weir_cockerham_fst()`

and`allel.stats.fst.windowed_weir_cockerham_fst()`

. - Added functions implementing the Hudson (1992) estimator for Fst:
`allel.stats.fst.hudson_fst()`

and`allel.stats.fst.windowed_hudson_fst()`

. - Added new module
`allel.stats.ld`

with functions for calculating linkage disequilibrium estimators, including`allel.stats.ld.rogers_huff_r()`

for pairwise variant LD calculation,`allel.stats.ld.windowed_r_squared()`

for windowed LD calculations, and`allel.stats.ld.locate_unlinked()`

for locating variants in approximate linkage equilibrium. - Added function
`allel.plot.pairwise_ld()`

for visualising a matrix of linkage disequilbrium values between pairs of variants. - Added function
`allel.model.create_allele_mapping()`

for creating a mapping of alleles into a different index system, i.e., if you want 0 and 1 to represent something other than REF and ALT, e.g., ancestral and derived. Also added methods`allel.model.GenotypeArray.map_alleles()`

,`allel.model.HaplotypeArray.map_alleles()`

and`allel.model.AlleleCountsArray.map_alleles()`

which will perform an allele transformation given an allele mapping. - Added function
`allel.plot.variant_locator()`

ported across from anhima. - Refactored the
`allel.stats`

module into a package with sub-modules for easier maintenance.

## v0.9¶

- Added documentation for the functions
`allel.bcolz.carray_from_hdf5()`

,`allel.bcolz.carray_to_hdf5()`

,`allel.bcolz.ctable_from_hdf5_group()`

,`allel.bcolz.ctable_to_hdf5_group()`

. - Refactoring of internals within the
`allel.bcolz`

module.

## v0.8¶

- Added subpop argument to
`allel.model.GenotypeArray.count_alleles()`

and`allel.model.HaplotypeArray.count_alleles()`

to enable count alleles within a sub-population without subsetting the array. - Added functions
`allel.model.GenotypeArray.count_alleles_subpops()`

and`allel.model.HaplotypeArray.count_alleles_subpops()`

to enable counting alleles in multiple sub-populations in a single pass over the array, without sub-setting. - Added classes
`allel.model.FeatureTable`

and`allel.bcolz.FeatureCTable`

for storing and querying data on genomic features (genes, etc.), with functions for parsing from a GFF3 file. - Added convenience function
`allel.stats.distance.pairwise_dxy()`

for computing a distance matrix using Dxy as the metric.

## v0.7¶

- Added function
`allel.io.write_fasta()`

for writing a nucleotide sequence stored as a NumPy array out to a FASTA format file.

## v0.6¶

- Added method
`allel.model.VariantTable.to_vcf()`

for writing a variant table to a VCF format file.