A new function ts_ibd() has been added, representing
an R interface to the tskit method
TreeSequence.ibd_segments(). However, note that
ts_ibd() returns IBD results as a data frame (optionally, a
spatially annotated sf data frame). The function does not
operate around iteration, as does its Python counterpart in _tskit. (PR
#123)
Emergency fix for a CRAN warning which suddenly popped up in latest CRAN checks. (#5600a4)
library(slendr)! Using the coalescent msprime back
end and slendr’s tree-sequence functions now requires making an
explicit call to a new function init_env() after
library(slendr) is executed. (PR #102)Motivation for the change: A small proportion of users have been experiencing issues with broken conda environments and various other issues with Python virtual environments in general. It’s hard to guess how frequent this has been, but experience from workshops and courses suggests perhaps 1 in 20 of users experiencing Python issues which hindered their ability to use slendr .(Fun fact: the first user-submitted GitHub issue upon releasing the first version of the slendr R package was… a Python virtual environment issue).
Explanation: Activating Python environments automatically upon
calling library(slendr) has been a popular feature because
it hid away most of the complexities of the R-Python interface that
powers slendr’s tree-sequence functionality. This was
particularly convenient for many slendr users, particularly
those who have no experience with Python at all.
Unfortunately, in cases where a Python virtual environments with
tskit/msprime/pyslim on a user’s system ended up corrupted (or if
anything else at the Python level got broken), the automatic Python
environment activation performed by the library(slendr)
call failed and slendr was not even loaded. Sadly, this
completely pulled the rug from under slendr and there was
nothing that could be done about it from its perspective (the issue
happened at a low-level layer of embedded-Python before slendr
could’ve been loaded into R). Solving these issues was not difficult for
experienced users, but many slendr users have no experience
with Python at all, they have never used conda, they don’t understand
the concept of “Python virtual environments” or how the R-Python
interface works. And nor should they! After all, slendr is an R
package.
Splitting the Python virtual environment activation step into its own
init_env() function means that library(slendr)
now always succeeds (regardless of potential underlying Python issues on
a user’s sytem), making it much easier to diagnose and fix Python
problems from R once the package is loaded.
So, to recap: library(slendr) no longer activates
slendr’s isolated Python virtual environment. In order to
simulate tree sequences and analyse them using its interface to
tskit, it is necessary to call init_env(). This
function performs the same Python-activation steps that
library(slendr) used to call automagically in earlier
slendr versions. No other change to your scripts is
necessary.
Related to the previous point: slendr now requires
Python 3.11, msprime 1.2.0, tskit 0.5.4, and pyslim 1.0.1, to keep up
with recent releases of its Python dependencies. Again, this presents no
hassle to the user, and the only thing required is re-running
setup_env(). (PR #112).
When a named list is provided as a sample_sets =
argument to a oneway statistic function, the names are used in a
set column of the resulting data frame even if only single
samples were used. (#2a6781)
It is now possible to have non-spatial populations in an
otherwise spatial model. Of course, when plotting such models on a map,
only spatial components of the model will be plotted and slendr
will give a warning. To be absolutely sure that users intends to do
this, slendr will also give a warning when running
compile_model() on models like this. Please consider this
option experimental for the time-being as it is hard to predict which
edge cases might break because of this (all unit tests and documentation
tests are passing though). Feedback is more than welcome. (PR #112).
It is now possible to label groups of samples in
slendr’s tskit interface functions which should make
data frames with statistics results more readable. As an example,
running
ts_f3(ts, A = c("p1_1", "p1_2", "p1_3"), B = c("p2_1", "p2_3"), C = c("p3_1", "p3_2", "p3_"))
resulted in a following data-frame output:
> ts_f3(ts, A = c("p1_1", "p1_2", "p1_3", "p1_4", "p1_5"),
B = c("p2_1", "p2_2", "p2_3"),
C = c("p3_1", "p3_2", "p3_3", "p3_4"))
# A tibble: 1 × 4
A B C f3
<chr> <chr> <chr> <dbl>
1 p1_1+p1_2+p1_3+p1_4+p1_5 p2_1+p2_2+p2_3 p3_1+p3_2+p3_3+p3_4 0.000130This gets unwieldy rather quickly, especially when dozens or hundreds
of samples are grouped together as populations. The new syntax allows
the following shortcut via customised group names leveraging the
standard named list functionality in R:
> ts_f3(ts, A = list(group_one = c("p1_1", "p1_2", "p1_3", "p1_4", "p1_5")),
B = list(group_two = c("p2_1", "p2_2", "p2_3")),
C = list(group_three = c("p3_1", "p3_2", "p3_3", "p3_4")))
# A tibble: 1 × 4
A B C f3
<chr> <chr> <chr> <dbl>
1 group_one group_two group_three 0.000130This is more readable and in line with some other
tskit-interface functions of slendr which used this
functionality via their sample_sets = argument
(ts_divergence(), ts_diversity(), etc.). (#ac5e484)
parent = argument of
population() is now NULL instead of
"ancestor". This prevents silly surprising clashes in
situation where some population’s name really is “ancestor”.
The only change internally is that for populations which are ancestral,
the splits data frame element of a slendr model
object which includes this population carries a formal “ancestral parent
population” as "__pop_is_ancestor" instead of just
"ancestor". Note that this is an internal implementation
detail and not something that particularly has to involve the user.
Still, if you have been somehow using slendr’s internal data
structures, keep this in mind. (#f8a39a2)The msprime() function now makes sure that a given
slendr model can fully coalesce to a single common ancestor
population. Previously, having multiple ancestral populations created
with parent = "ancestor" would cause an infinite simulation
when plugged into the msprime() backend. (#095b124)
The initial size of a population which emerges from a split from another population is now printed in a population history summary in the R console. (#6525bf3)
A couple of fixes to support loading, processing, and plotting of “manually” created tree sequences have been implemented (see this). Not sure how practically useful, but it’s important to be able to load even “pure” tree sequences which are not from simulators such as SLiM and msprime. A set of unit tests has been added, making sure that a minimalist nodes & edges table can be loaded, as well as nodes & edges & individuals, plus tables of populations and sites & mutations. PRs with more extensive unit tests and bug reports of tree sequences which are failing to load would be appreciated! The code for handling cases of “manually-created” tree sequences which have missing individual table, missing populations table, etc. seems especially brittle at the moment (#79adf14).
The -1 value as a missing value indicator used in
tskit is now replaced with the more R-like NA in various
tree-sequence tables (annotated by slendr or original through
tskit itself) (#79adf14).
Relative paths are now expanded in ts_save() (#382e0b7).
slendr models can now be optionally compiled without
serialization to disk. This only works with the msprime()
coalescent back end but will be much faster in cases where a huge number
of simulations needs to be run because for non-serialized models,
msprime() now calls the back end engine directly through
the R-Python interface (rather than on the command line) and output tree
sequences are not saved to disk, rather than passed through the Python-R
interface directly in memory (PR #112).
Deprecated argument sampling = of the functions
slim() and msprime() has now been permanently
removed in favour of the samples = argument (#0757b6e).
Avoid the unnecessary array type of tskit
results returned via reticulate. Numeric vectors (columns of data frames
with numerical results) obtained in this way are simple R numeric vector
(#5101b39).
One-way and multi-way statistics results are now returned as
simple numerical vectors. Previously, results were returned as a type
array despite “looking” as vectors (this is how values are
returned to R from the reticulate-Python layer), which caused
unnecessary annoyances and type-conversions on the R side of things and
was not even intended (#403df3b).
Computing population genetic statistics on named samples that are not present in a tree sequence (most likely typos) is now correctly caught and reported as an error (#da7e0bb).
SLiM 4.0 is now required for running simulations with the
slim() engine. If you want to run slendr
simulations with SLiM (spatial or non-spatial), you will need to upgrade
you SLiM installation. SLiM 3.7.1 version is no longer supported as the
upcoming new slendr spatial features will depend on SLiM 4.x
and maintaining two functionally identical yet syntactically different
back ends is not feasible (PR #104).
At the same time as the SLiM 4.0 release, new versions of Python
modules msprime, tskit and pyslim have also been released. In fact, to
be able to work with SLiM 4.0 tree sequences properly, those Python
modules must be upgraded as well. Next time you load
library(slendr), you will be prompted to setup a new
updated Python environment which you can do easily by running
setup_env().
Experimental support for running coalescent msprime simulations and analysing tree-sequence data using tskit on the Windows platform has now been implemented (PR #102).
slendr is now on CRAN!
Big changes to the way tree-sequence outputs are handled by
slendr by default. See this
comment for an extended description and examples of the change. (PR
#100). Briefly,
simulation functions slim() and msprime() now
return a tree-sequence object by default (can be switched off by setting
load = FALSE), avoiding the need to always run
ts <- ts_load(model) as previously. At the same time, a
parameter output = can be now used in slim()
and msprime() to specify the location where a tree-sequence
file should be saved (temporary file by default).
slendr’s tree-sequence R interface to the tskit Python module has been generalized to load, process, and analyze tree sequences from non-slendr models! This means that users can use the slendr R package even for analyzing tree sequences coming from standard msprime and SLiM scripts, including all spatial capabilities that have been only available for slendr tree sequences so far. Please note that this generalization is still rather experimental and there might be corner cases where a tree sequence from your msprime or SLiM script does not load properly or leads to other errors. If this happens, please open a GitHub issue with the script in question attached. (PR #91)
Removed functions and some function arguments originally
deprecated during the renaming phase of the pre-preprint refactoring.
This affects compile, boundary,
dispersal, expand, geneflow,
plot.slendr, plot_graph, read,
sampling, and shrink. Similarly, deprecated
dir argument of the compile_model is now
path, geneflow argument of
compile_model is now gene_flow, and the
_dist suffix was removed from
competition_dist, mate_dist, and
dispersal_dist. If you get an error about a missing
function or a function argument in code which used to work in an ancient
version of slendr, this is why. (#985b451)
When setting up an isolated Python environment using
setup_env(), slendr now makes a decision whether
to install Python dependencies using pip (critical on osx-arm64 for
which the conda msprime/tskit are unfortunately currently broken) or
with conda (every other platform). This can be still influenced by the
user using the pip = <TRUE|FALSE> argument, but we
now change the default behavior on ARM64 Mac. (#54a413d)
The name of the default slendr Python environment is now
shortened even more, and the redundant _pandas prefix is
now dropped. Users will be notified upon calling
library(slendr) that a new environment should be created.
This is OK, it’s not a bug. (#54a413d)
The format of the default slendr Python environment is
now
msprime-<version>_tskit-<version>_pyslim-<version>_pandas,
dropping the slendr_ prefix. This paves the way towards a
future non-slendr tskit R package, which will share the same
Python environment with slendr (because both R packages will go
hand in hand). This isn’t really a user-facing change, except that
calling setup_env() will suggests creating a new Python
environment and library(slendr) will appear as if a
slendr environment is not yet present. Calling
setup_env() and creating a new Python environment from
scratch will solve the problem. (#eb05180)
xrange and yrange parameters of
world() are now enforced to be two-dimensional numeric
vectors, avoiding unnecessary issues with misspecified
longitude/latitude (#df95369)
The argument sampling = in slim() and
msprime() is now renamed to samples = (#adf4e0d).
The automated setup_env() function for creating
dedicated mini Python environments for slendr now installs
packages using pip by default. Reason: The rate of conda
failures and dependency conflicts (even in the trivial case of
installing nothing more than msprime + tskit +
pyslim + pandas) is too high to rely on it. The option
to use conda for package installations with setup_env() is
still there, but the users must explicitly call
setup_env(pip = FALSE) to get this behavior. Note that
conda is still used as a means to install Python itself! This change
only affects the way how Python modules are installed into a dedicated
slendr Python environment, not the installation of Python
itself. (#81be1a7)
The name of the automatically created slendr-specific Python environment is now composed from the names and versions of Python modules installed. This makes it possible to naturally upgrade both slendr and its Python dependencies in case the tskit / msprime / pyslim folks upgrade some of those packages. In that case, if a slendr user upgrades the slendr package (and that new version requires newer versions of Python modules), slendr will simply recommend to create a new Python environment without additional effort on our part. (#81be1a7)
The code of setup_env() was simplified to bare
essentials. Now it only serves as a way to auto-setup a
dedicated, isolated Python installation and slendr environment.
The interface to install Python modules into custom-defined Python
environment created outside R has been removed because this
functionality is not necessary – these custom environments can be easily
activated by calling reticulate::use_virtualenv or
reticulate::use_condaenv. (#30f24b9)
If some Python users want to use custom Python environments with
msprime, tskit, and pyslim, they can silence
the suggestion to use setup_env() printed by the
library(slendr) call by setting
options(slendr.custom_env = TRUE). (#30f24b9)
The argument sim_length = is now renamed to
simulation_length =. Both are accepted for the moment and
using the old name will simply inform the user of the future
deprecation. (#56491fb)
Extensive set of runnable examples including figures and a built-in pre-compiled example model have been added to the documentation. (#395df62c)