A massive update introducing the possibility of
simulating non-neutral models with slim() has been
introduced. This update is too big to describe in the changelog – for
more information and motivation, see the description in the
associated PR, or the
new extensive vignette on the topic. Feedback on this functionality,
missing features, and bug reports are highly appreciated! (PR #155)
The behavior previously implemented via the output =
and ts = arguments of slim() (and
msprime()) has been changed to facilitate more
straightforward handling of output paths in user-defined SLiM extensions
and other packages leveraging slendr for inference. The
slim() and msprime() function interfaces are
now simplified in the following way:
slim(): the ts argument is now logical.
TRUE switches on tree-sequence recording,
FALSE switches it off. If tree-sequence recording is on
(the default setting), the function automatically returns a
tree-sequence R object. If users want to save it to a custom location,
they should use the function ts_write() on the returned
tree-sequence object. If customized output files are to be produced via
user-defined extension scripts, those scripts can use a
slendr/SLiM constant PATH, which is always
available in the built-in SLiM script and which can be set from R via
slim(..., path = <path to a directory>). In that
case, the slim() function always returns that path back.
Crucially, in this case slim() will not return a tree
sequence object, but that object can be loaded by
ts_read("<path to a directory>/slim.trees"). In other
words, nothing changes for the usual SLiM-based slendr
workflow, but for models generating custom output files, a small amount
of work is needed to load the tree sequence – the tree-sequence file
outputs are therefore treated exactly the same way as non-tree-sequence
user-defined output files. As a result of these changes,
slim() no longer accepts a load = TRUE|FALSE
argument.The above is implemented in PR #157.
ts_genotypes() now works even for
non-slendr tree sequences, which do not have slendr
individual names of samples in the ts_nodes() output. (#d348ec)
Due to frequent issues with installation of Python dependencies
of slendr in a completely platform independent way (in the
latest instance this being conda installation of pyslim crashing on
M-architecture Macs), setup_env() now only uses conda to
install msprime and tskit – pyslim and
tspop are always installed via pip regardless of whether
setup_env(pip = FALSE) (the default) or
setup_env(pip = TRUE) is used. (#408948)
A new function extract_parameters() can extract
parameters of either a compiled slendr model object or a tree
sequence simulated from a slendr model. This can be useful
particularly for simulation-based inferences where model parameters are
often drawn from random distributions and there’s a need to know which
parameters of a model (split times, gene-flow rates, etc.) have been
drawn. (#3632bd0)
compile_model() now allows to specify a description
of time units used while scheduling slendr model events. This
has purely descriptive purpose – in particular, these units are used in
model plotting functions, etc. (#9b5b7ea0)
The slim_script argument of
compile_model() has been replaced by extension
argument, which allows users to provide their custom-designed SLiM
snippets for extending the behavior of slendr’s SLiM simulation
engine. (#d11ac7)
The sim_length argument of
compile_model() has been removed following a long period of
deprecatiaon. (#12da50)
When a named list of samples is used as X input to
ts_f4ratio(), the name of the element is used in the
X column of the resulting data frame. (#0571a6)
ts_table() can now extract the “sites”
tskit table as ts_table(ts, "sites"). (#e708f2)
When applied to slendr tree sequences,
ts_recapitate() no longer issues the warning:
TimeUnitsMismatchWarning: The initial_state has time_units=ticks but time is measured in generations in msprime. This may lead to significant discrepancies between the timescales. If you wish to suppress this warning, you can use, e.g., warnings.simplefilter('ignore', msprime.TimeUnitsMismatchWarning).
For slendr tree sequences, ticks are the same thing as
generations anyway. (#43c45083)
Running slim(..., method = "gui") was broken due to
recent changes to make slendr work on Windows. A path to a
generated SLiM script executed in SLiMgui was incorrectly normalized.
Non-SLiMgui runs were not affected. (#ccae1df)
A new helper function get_env() now returns the name
of the built-in slendr Python environment (without activating
it). (#162ccc)
clear_env() now has a new argument
all = (TRUE|FALSE) which allows deleting all
slendr Python environments. Previously, the function always
removed only the recent environment, which lead to the accumulation of
potentially large number of slendr environments over time. (#8707b9)
plot_model() has a new optional argument
samples = which will – when set to a result of a sampling
schedule created by schedule_sampling() – visualize the
counts of samples to be recorded at each given time-point. (#d72ac5)
The msprime dependency of slendr has been
updated to version 1.3.1. As a result, setup_env() will
have to be re-run to update the internal slendr Python
environment. (#dcb83d)
A full support for running SLiM and msprime simulations
with slendr and for analyzing tree sequences using its tskit interface has been
implemented. Please note that the Windows support is still rather
experimental – the internal slendr test suite currently assumes
that SLiM has been installed using the msys2 system as
described in the section 2.3.1 of the SLiM manual and other means of
installing SLiM (such as via conda) might require additional
adjustments. A fallback option in the form of the
slim_path= argument of the slim() function can
be used in non-standard SLiM installation circumstances. For most
convenience, please add the path to the directory containing the
slim.exe binary to the PATH variable by
editing the C:/Users/<username>/Documents/.Renviron
file accordingly. See the relevant section on Windows installation in
the slendr
documentation for additional information. Feedback on the Windows
functionality and bug reports are highly appreciated via GitHub issues!
Many thanks to @GKresearch and @rdinnager for their huge help in making
the Windows port happen! (PR #149)
A trivial change has been made to slendr’s SLiM back-end script fixing the issue introduced in a SLiM 4.1 upgrade (see changelog for version 0.8.1 below). This is not expected to lead to different simulation outputs between the two versions of slendr (0.8.2 vs 0.8.1) or SLiM (4.1 vs 4.0.1) used. (PR #148)
The msprime internal dependency of slendr was
updated to 1.3.0, and Python to 3.12. As a result, after loading
slendr, users will be prompted to re-run
setup_env() to make sure that the dedicated slendr
Python environment is fully updated. At the same time, this prevents a
failing installation on (at the very least) M1 macOS using
pip. (#5ce212, #a210d4)
Fixed an issue of apparent contradiction in time direction in models where range expansion was scheduled within some time interval together with associated “locked-in” changes in population size over that time interval. (#d2a29e)
The introduction of tspop which is only installable via
pip (see changelog for the previous version) caused GLIBCXX-related
errors between conda and pip dependencies related to the pandas Python
package. To work around this issue, setup_env() no longer
installs pandas from conda regardless of the setting of the
pip = TRUE|FALSE parameter. Instead, pandas is
installed via pip in a single step when tspop is being
installed. (#cbe960)
WARNING: SLiM 4.1 which has just been released
includes a couple of backwards
incompatible changes related to the implementation of spatial maps
which prevent the current version of slendr’s
slim() function from working correctly. If you rely on the
functionality provided by the slim() function, you will
have to use SLiM 4.0. (Note that if you want to have multiple versions
of SLiM on your system, you can either use the slim_path =
argument of slim() or specify the $PATH to the
required version of SLiM in your ~/.Renviron file just like
you do under normal circumstances). Porting slendr for SLiM 4.1
is being worked on.
In order to support the new ts_tracts() function
backed by the tspop module (see the item below), a new
slendr Python environment is required. As such, users will have
to run setup_env() to get all the required Python
dependencies which will be now installed in the internal virtual
environment named
Python-3.11_msprime-1.2.0_tskit-0.5.6_pyslim-1.0.4_tspop-0.0.2.
(#b5330c)
Experimental support for the tspop
link-ancestors algorithm for detection of ancestry tracts
in the form of a new slendr function ts_tracts().
Only works on slendr-generated msprime tree sequences
and “pure” msprime and SLiM tree sequences (not
slendr-generated SLiM tree sequences), and has been only tested
on a few toy models. Note: the tspop Python
module is not published on conda. In order to set up a new
slendr Python environment, you will have to run
setup_env(pip = TRUE) to make sure that Python dependencies
are installed with pip instead of conda. (PR #145)
Updated Python dependencies (bugfix pyslim release v1.0.4
and tskit v0.5.6,
the latter due to a broken jsonschema dependency of tskit).
(#001ee5)
Experimental support for manually created spatial tree sequences. (PR #144)
A new function ts_names() has been added, avoiding
the need for the extremely frequent (and, unfortunately, cumbersome)
trick of getting named lists of individual symbolic names
ts_samples(ts) %>% split(., .[[split]]) %>% lapply([[, "name")
which is very confusing for all but the more experienced R users. (#7db6ea)
Fixed broken concatenation of symbolic sample names in tree-sequence statistic functions, when those were provided as unnamed single-element lists of character vectors. (#b3c650)
plot_model() now has an argument
file =, making it possible to save a visualization of a
model without actually opening a plotting device. This can be useful
particularly while working on a remote server, in order to avoid the
often slow X11 rendering. (#e60078)
plot_model() now has an argument
order = allowing to override the default in-order
ordering of populations along the x-axis. (#7a10ea)
install.packages(c("sf", "stars", "rnaturalearth")) away!
The difference is that slendr doesn’t try to do this during its
own installation, but users are instructed to do this themselves (if
needed) when the package is loaded. (#7a10ea)If spatial dependencies are not present but a spatial slendr
function is called regardless (such as world(),
move(), etc.), an error message is printed with an
information on how to install spatial dependencies via
install.packages() as above.
Why? It’s true that the main reason for slendr’s existence is its ability to simulate spatio-temporal data on realistic landscapes via SLiM. However, in practice, most of the “average” uses of slendr in the wild (and in classrooms!) rely on its traditional, non-spatial interface, with its spatial features being used comparatively rarely at the moment (except for some cutting-edge exploratory research). Given that setting up all of the spatial dependencies can be a bit of a hurdle, we have decided to make these dependencies optional, rather than force every user to go through the process of their installation whether they need the spatial features or not.
A function check_dependencies() is now exported and
can be used to check whether a slendr Python environment () or
SLiM () are present. This is useful for other software building upon
slendr, normal users can freely ignore this. (#6ae6ce)
A path to a file from which a tree sequence was loaded from is
now tracked internally via a
attr(<tree sequence>, "path") attribute. Note that
this has been implemented for the purposes of clean up for large-scale
simulation studies (such as those facilitated by demografr) as a
mostly internal feature, and should be considered experimental. (#f181a2)
Attempts to resize a population right at the time of the split (which led to issues with simulations) are now prevented. (#f181a2)
Fix for a minor issue preventing sampling an msprime population right at the time of its creation. (#aea231)
This is an emergency upgrade to match the latest pyslim 1.0.3 due to a serious bug in recapitation. See here and here for an extensive discussion during the process of identification of the bug and its eventual fix. For a brief summary of the practical consequences of this bug, see this thread by pyslim’s developer and its formal announcement here.
This change will require you to re-run setup_env()
in order to update slendr’s Python internals by creating a new internal
Python virtual environment. (#45539a)
A potential issue with a parent population being scheduled for
removal before a daughter population splits from it is now caught at the
moment of the daughter population() call rather than during
a simulation slim() run. (#0791b5)
The function plot_model() has a new argument
gene_flow=<TRUE|FALSE> which determines whether
gene-flow arrows will be visualized or not. (#104aa6)
The possibility to perform recapitation, simplification, or
mutation of a tree sequence right inside a call to
ts_read() (by providing recapitate = TRUE,
simplify = TRUE, and mutate = TRUE, together
with their own arguments) has now been removed. The motivation for this
change is the realization that there is no benefit of doing things like
ts_read("<path>", recapitate = TRUE, Ne = ..., recombination_rate = ...)
over
ts_read("<path>") %>% ts_recapitate(Ne = ..., recombination_rate = ...),
and the frequent confusion when recapitate = TRUE or other
switches are forgotten by the user. All slendr teaching
material and most actively used research codebases I know of use the
latter, more explicit, pipeline approach anyway, and this has been the
one example where reduncancy does more harm than good. (#ad82ee)
Note: Loading library(slendr) will
prompt a message “The legacy packages maptools, rgdal, and rgeos,
underpinning the sp package, which was just loaded, will retire in
October 2023. […].” This is an internal business of packages used
by slendr which unfortunately cannot be silenced from
slendr’s side. There’s no reason to panic, you can safely
ignore them. Apologies for the unnecessary noise.
This is a relatively large update, which unfortunately had to be released in haste due to the retirement of the rgdal package – a significant dependency of the entire spatial R ecosystem which is being phased out in the effort to move towards modern low-level geospatial architecture. Although slendr itself does not depend on rgdal, many of its dependencies used to (but won’t in the short term, hence the push to remove the rgdal dependency). The most significant update has been the addition of IBD functionality of tskit, as described below. However, large part of this functionality has not been extensively tested and should be considered extremely experimental at this stage. If you would like to use it, it might be safer to either wait for a later release in which the IBD functionality will be more stable, or use the underlying, battle-tested Python implementation in tskit.
ts_ibd() now returns the ID number of a MRCA node of
a pair of nodes sharing a given IBD segment, as well as the TMRCA of
that node. (#7e2825)
Trivial parameter errors are caught during
population() calls rather than during simulation (solving
minor issues discovered via big simulation runs during the development
of demografr). (#e33373)
Fix error in plotting exponential resizes which do not last until “the present”. (#4c49a4)
ts_ibd() no longer gives obscure error when
between = is provided as a named list of individuals’ names
(instead of an expected unnamed list). The names of list elements are
not used in any way, but the error happens somewhere deeply in the
R->Python translation layer inside reticulate and
there’s no need for the users to concern themselves with it. (#7965e4)
Population size parameters and times are now explicitly converted to integer numbers. This is more of an internal, formal change (the conversion has been happening implicitly inside the SLiM engine anyway) but is now explicitly stated, also in the documentation of each relevant function. (#b7e89e)
Population names are now restricted to only those strings which
are also valid Python identifiers. Although this restriction is only
needed for the msprime back end of slendr (not SLiM), it makes
sense to keep things tidy and unified. This fixes msprime crashing with
ValueError: A population name must be a valid Python identifier.
(#4ef518)
The layout algorithm of plot_model() has been
improved significantly. (PR #135).
A new optional argument run = has been added to
slim() and msprime(). If set to
TRUE (the default), the engines will operate the usual way.
If set to FALSE, no simulation will be run and the
functions will simply print a command-line command to execute the engine
in question (returning the CLI command invisibly). (#2e5b85)
The following start-up note is no longer shown upon calling
library(slendr):
NOTE: Due to Python setup issues on some systems which have been
causing trouble particularly for novice users, calling library(slendr)
no longer activates slendr's Python environment automatically.
In order to use slendr's msprime back end or its tree-sequence
functionality, users must now activate slendr's Python environment
manually by executing init_env() after calling library(slendr).
(This note will be removed in the next major version of slendr.)Users have to call init_env() to manually activate the
Python environment of slendr (see note under version 0.5.0 below for an
extended explanation).
ts_simplify() now accepts optional arguments
keep_unary and keep_unary_in_individuals (see
the official tskit
docs for more detail) (#1b2112)
Fix for ts_read() failing to load
slendr-produced tree sequences after they were simplified down
to a smaller set of sampled individuals (reported here). The issue
was caused by incompatible sizes of the sampling table (always in the
same form as used during simulation) and the table of individuals stored
in the tree sequence after simplification (potentially containing a
smaller set of individuals than in the original sampling table). To fix
this, slendr tree sequence objects now track information about
which individuals are regarded as “samples” (i.e. those with symbolic
names) which is maintained through simplification, serialization and
loading, and used by slendr’s internal machinery during join
operations. (PR #137)
Metadata summary of ts_nodes() results is no longer
printed whenever typed into the R console. Instead, summary can be
obtained by explicit call to summary() on the
ts_nodes() tables. (#01af51
ts_tree() and ts_phylo() now extract
trees based on tskit’s own zero-based indexing #554e13.
ts_simplify() now accepts
filter_nodes = TRUE|FALSE, with the same behavior to
tskit’s own
method #f07ffed.
This minor release implements an emergency fix for a CRAN warning which suddenly popped up in latest CRAN checks. (#5600a4)
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. Until the next major version of slendr, this
function should be considered experimental. (PR #123)
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_write() (#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_read(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)