Welcome to msprime’s documentation!ΒΆ
This is the documentation for msprime
, a reimplementation of Hudson’s
classical ms simulator.
Contents:ΒΆ
IntroductionΒΆ
The primary goal of msprime
is to efficiently and conveniently
generate coalescent trees for a sample under a range of evolutionary
scenarios. The library is a reimplementation of Hudson’s seminal
ms
program, and aims to eventually reproduce all its functionality.
msprime
differs from ms
in some important ways:
msprime
is much more efficient thanms
, both in terms of memory usage and simulation time. In fact,msprime
is also much more efficient than simulators based on approximations to the coalescent with recombination model, especially for simulations with very large sample sizes.msprime
can easily simulate chromosome sized regions for hundreds of thousands of samples.msprime
is primarily designed to be used through its Python API to simplify the workflow associated with running and analysing simulations. (However, we do provide anms
compatible command line interface to plug in to existing workflows.) For many simulations we first write a script to generate the command line parameters we want to run, then fork shell processes to run the simulations, and then parse the results to obtain the genealogies in a form we can use. Withmsprime
all of this can be done directly in Python, which is both simpler and far more efficient.msprime
does not use Newick trees for interchange as they are extremely inefficient in terms of the time required to generate and parse, as well as the space required to store them. Instead, we use a welldefined format using the powerful HDF5 standard. This format allows us to store genealogical data very concisely, particularly for large sample sizes.
InstallationΒΆ
Quick StartΒΆ
To install and run msprime
on a fresh Ubuntu 15.10 installation, do the
following:
$ sudo aptget install pkgconfig pythondev pythonpip libgsl0dev hdf5tools libhdf5serialdev
$ sudo pip install msprime
$ mspms 2 1 t 1
/usr/local/bin/mspms 2 1 t 1
5338 8035 23205
//
segsites: 3
positions: 0.014 0.045 0.573
100
011
If you do not wish to install msprime
to your system, you can try
it out in a virtualenv as
follows:
$ virtualenv msprimeenv
$ source msprimeenv/bin/activate
(msprimeenv) $ pip install msprime
(msprimeenv) $ mspms
See below for installation instructions for Macs.
RequirementsΒΆ
Msprime requires Python 2.7+ (Python 3 versions are fully supported from 3.1 onwards), the GNU Scientific Library, and HDF5 version 1.8 or later. These packages are available for all major platforms. For example, to install on Debian/Ubuntu use:
# aptget install pythondev libgsl0dev libhdf5serialdev pkgconfig
For Redhat/Fedora use:
# yum install gsldevel hdf5devel
On FreeBSD we can use pkg
to install the requirements:
# pkg install gsl hdf518
To install the dependencies on OS X, we can use Homebrew:
$ brew update
$ brew install gsl homebrew/science/hdf5
InstallationΒΆ
The simplest method of installation is to use PyPI and pip:
# pip install msprime
This will work in most cases, once the Requirements have been satisfied. See below for platform specific build instructions when this fails.
If you do not have root access to your machine, you can install
msprime
into your local Python installation as follows:
$ pip install msprime user
To use the mspms
program you must ensure
that the ~/.local/bin
directory is in your PATH
, or
simply run it using:
$ ~/.local/bin/mspms
To uninstall msprime
, simply run:
$ pip uninstall msprime
Platform specific installationΒΆ
This section contains instructions to build on platforms that require build time flags for GSL and HDF5.
FreeBSD 10.0ΒΆ
Install the prerequisitites, and build msprime
as follows:
# pkg install gsl hdf518
# CFLAGS=I/usr/local/include LDFLAGS=L/usr/local/lib pip install msprime
This assumes that root is logged in using a bash shell. For other shells,
different methods are need to set the CFLAGS
and LDFLAGS
environment
variables.
OS XΒΆ
First, ensure that Homebrew is installed and uptodate:
$ brew update
We need to ensure that the version of Python we used is installed via Homebrew (there can be issues with linking to HDF5 if we use the builtin version of Python or a version from Anaconda). Therefore, we install Python 3 using homebrew:
$ brew install python3
$ pip3 install upgrade pip setuptools
The previous step can be skipped if you wish to use your own Python installation, and already have a working pip.
Now install the dependencies and msprime:
$ brew install gsl homebrew/science/hdf5
$ pip3 install msprime
Check if it works:
$ mspms 10 1 T
TutorialΒΆ
This is the tutorial for the Python interface to the msprime
library. Detailed API Documentation is also available for this
library. An mscompatible command line interface
is also available if you wish to use msprime
directly within
an existing work flow.
Simulating treesΒΆ
Running simulations is very straightforward in msprime
:
>>> import msprime
>>> tree_sequence = msprime.simulate(sample_size=5, Ne=1000)
>>> tree = next(tree_sequence.trees())
>>> print(tree)
{0: 5, 1: 7, 2: 5, 3: 7, 4: 6, 5: 6, 6: 8, 7: 8, 8: 1}
Here, we simulate the coalescent for a sample of size
5 with an effective population size of 1000,
and then print out a summary of the resulting tree. The
simulate()
function returns a
TreeSequence
object, which provides a very
efficient way to access the correlated trees in simulations
involving recombination. In this example we know that
there can only be one tree because we have not provided
a value for recombination_rate
, and it
defaults to zero. Therefore, we access the only tree in the
sequence using the call next(tree_sequence.trees())
.
Trees are represented within msprime
in a slightly unusual way. In
the majority of libraries dealing with trees, each node is
represented as an object in memory and the relationship
between nodes as pointers between these objects. In msprime
,
however, nodes are integers: the leaves (i.e., our sample) are the
integers \(0\) to \(n  1\), and every internal node is
some positive integer \(\geq n\). The result of printing
the tree is a summary of how these nodes relate to each other
in terms of their parents. For example, we can see that the parent
of nodes 1 and 3 is node 7.
This relationship can be seen more clearly in a picture:
This image shows the same tree as in the example but drawn out in
a more familiar format (images like this can be drawn for any
tree using the draw()
method).
We can see that the leaves of the tree
are labelled with 0 to 4, and all the internal nodes of the tree
are also integers with the root of the tree being 8. Also shown here
are the times for each internal node in generations. (The
time for all leaves is 0, and so we don’t show this information
to avoid clutter.)
Knowing that our leaves are 0 to 4, we can easily trace our path
back to the root for a particular sample using the
get_parent()
method:
>>> u = 0
>>> while u != msprime.NULL_NODE:
>>> print("node {}: time = {}".format(u, tree.get_time(u)))
>>> u = tree.get_parent(u)
node 0: time = 0.0
node 5: time = 107.921165302
node 6: time = 1006.74711128
node 8: time = 1785.36352521
In this code chunk we iterate up the tree starting at node 0 and
stop when we get to the root. We know that a node is the root
if its parent is msprime.NULL_NODE
, which is a special
reserved node. (The value of the null node is 1, but we recommend
using the symbolic constant to make code more readable.) We also use
the get_time()
method to get the time
for each node, which corresponds to the time in generations
at which the coalescence event happened during the simulation.
We can also obtain the length of a branch joining a node to
its parent using the get_branch_length()
method:
>>> print(tree.get_branch_length(6))
778.616413923
The branch length for node 6 is 778.6 generations as the time for node 6 is 1006.7 and the time of its parent is 1785.4. It is also often useful to obtain the total branch length of the tree, i.e., the sum of the lengths of all branches:
>>> print(tree.get_total_branch_length())
>>> 5932.15093686
RecombinationΒΆ
Simulating the history of a single locus is a very useful, but we are most
often interesting in simulating the history of our sample across large genomic
regions under the influence of recombination. The msprime
API is
specifically designed to make this common requirement both easy and efficient.
To model genomic sequences under the influence of recombination we have
two parameters to the simulate()
function.
The length
parameter specifies the length of the simulated sequence
in bases, and may be a floating point number. If length
is not
supplied, it is assumed to be 1. The recombination_rate
parameter specifies the rate of crossing over per base per generation,
and is zero by default. See the API Documentation for a discussion of the precise
recombination model used.
Here, we simulate the trees across over a 10kb region with a recombination rate of \(2 \times 10^{8}\) per base per generation, with an effective population size of 1000:
>>> tree_sequence = msprime.simulate(
... sample_size=5, Ne=1000, length=1e4, recombination_rate=2e8)
>>> for tree in tree_sequence.trees():
... print(tree.get_interval(), str(tree), sep="\t")
(0.0, 4701.4225005874) {0: 6, 1: 5, 2: 6, 3: 9, 4: 5, 5: 7, 6: 7, 7: 9, 9: 1}
(4701.4225005874, 10000.0) {0: 6, 1: 5, 2: 6, 3: 8, 4: 5, 5: 8, 6: 9, 8: 9, 9: 1}
In this example, we use the trees()
method to iterate over the trees in the sequence. For each tree
we print out the interval the tree covers (i.e., the genomic
coordinates which all share precisely this tree) using the
get_interval()
method. Thus, the first tree covers the
first 4.7kb of sequence and the second tree covers the remaining 5.3kb.
We also print out the summary of each tree in terms of the parent values for
each tree. Again, these differences are best illustrated by
some images:
(We have suppressed the node time labels here for clarity.) We can see that these trees share a great deal of their structure, but that there are also important differences between the trees.
Warning
Do not store the values returned from the
trees()
iterator in a list and operate
on them afterwards! For efficiency reasons msprime
uses the same
instance of SparseTree
for each tree in the sequence
and updates the internal state for each new tree. Therefore, if you store
the trees returned from the iterator in a list, they will all refer
to the same tree.
MutationsΒΆ
Mutations are generated in msprime
by throwing mutations down
on the branches of trees at a particular rate. The mutations are
generated under the infinite sites model, and so each mutation
occurs at a unique (floating point) point position along the
genomic interval occupied by a tree. The mutation rate for simulations
is specified using the mutation_rate
parameter of
simulate()
. For example, to add some mutations
to our example above, we can use:
>>> tree_sequence = msprime.simulate(
... sample_size=5, Ne=1000, length=1e4, recombination_rate=2e8, mutation_rate=2e8)
>>> print("Total mutations = ", tree_sequence.get_num_mutations())
>>> for tree in tree_sequence.trees():
>>> print(tree.get_interval(), list(tree.mutations()), sep="\t")
Total mutations = 1
(0.0, 4701.4225005874) []
(4701.4225005874, 10000.0) [Mutation(position=5461.212369738915, node=6, index=0)]
In this example (which has the same genealogies as our example above because
we use the same random seed), we have one mutation which
falls on the second tree. Mutations are represented as an object
with three attributes: position
is the location of the mutation
in genomic coordinates, node
is the node in the tree above which the
mutation occurs, and index
is the (zerobased) index of the mutation
in the list. Positions are given as a floating point value as we are
using the infinite sites model. Every mutation falls on exactly one tree
and we obtain the mutations for a particular tree using the
mutations()
method. Mutations are always returned
in increasing order of position. The mutation in this example is shown
as a red box on the corresponding branch:
We can calculate the allele frequency of mutations easily and
efficiently using the get_num_leaves()
which returns the number of leaves underneath a particular node.
For example,:
>>> for tree in tree_sequence.trees():
... for mutation in tree.mutations():
... print("Mutation @ position {} has frequency {}".format(
... mutation.position,
... tree.get_num_leaves(mutation.node) / tree.get_sample_size()))
Mutation @ position 5461.21236974 has frequency 0.4
Sometimes we are only interested in a subset of the mutations
in a tree sequence. In these situations, it is useful (and efficient)
to update the tree sequence to only include the mutations we are
interested in using the TreeSequence.set_mutations()
method.
Here, for example, we simulate some data and then retain only the
common variants where the allele frequency is greater than 0.5.
import msprime
def set_mutations_example():
tree_sequence = msprime.simulate(
sample_size=10000, Ne=1e4, length=1e7, recombination_rate=2e8,
mutation_rate=2e8)
print("Simulated ", tree_sequence.get_num_mutations(), "mutations")
common_mutations = []
for tree in tree_sequence.trees():
for mutation in tree.mutations():
p = tree.get_num_leaves(mutation.node) / tree.get_sample_size()
if p >= 0.5:
common_mutations.append(mutation)
tree_sequence.set_mutations(common_mutations)
print("Reduced to ", tree_sequence.get_num_mutations(), "common mutations")
Running this code, we get:
>>> set_mutations_example()
Simulated 78202 mutations
Reduced to 5571 common mutations
VariantsΒΆ
We are often interesting in accessing the sequence data that results from
simulations directly. The most efficient way to do this is by using
the TreeSequence.variants()
method, which returns an iterator
over all the variant objects arising from the trees and mutations.
Each variant contains all the information in a mutation object, but
also has the observed sequences for each sample in the genotypes
field.
import msprime
def variants_example():
tree_sequence = msprime.simulate(
sample_size=20, Ne=1e4, length=5e3, recombination_rate=2e8,
mutation_rate=2e8, random_seed=10)
print("Simulated ", tree_sequence.get_num_mutations(), "mutations")
for variant in tree_sequence.variants():
print(variant.index, variant.position, variant.genotypes, sep="\t")
In this example we simulate some data and then print out the observed
sequences. We loop through each variant and print out the observed state of
each sample as an array of zeros and ones, along with the index and position
of the corresponding mutation. (The default form for the
genotypes
array here is a numpy.ndarray
; however, the output can
also be a plain Python bytes object. See the TreeSequence.variants()
documentation for details.) Running the code, we get:
>>> variants_example()
Simulated 7 mutations
0 2146.29801511 [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
1 2475.24314909 [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
2 3087.04505359 [0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
3 3628.35359621 [1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1]
4 4587.85827679 [0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0]
5 4593.29453791 [1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1]
6 4784.26662856 [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
This way of working with the sequence data is quite efficient because we do not need to keep the entire variant matrix in memory at once.
import msprime
import numpy as np
def variant_matrix_example():
print("\nCreating full variant matrix")
tree_sequence = msprime.simulate(
sample_size=20, Ne=1e4, length=5e3, recombination_rate=2e8,
mutation_rate=2e8, random_seed=10)
shape = tree_sequence.get_num_mutations(), tree_sequence.get_sample_size()
A = np.empty(shape, dtype="u1")
for variant in tree_sequence.variants():
A[variant.index] = variant.genotypes
print(A)
In this example, we run the same simulation but this time store entire variant matrix in a twodimensional numpy array. This is useful for integrating with tools such as scikit allel.:
>>> variant_matrix_example()
Creating full variant matrix
[[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
[1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1]
[0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0]
[1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]]
Historical samplesΒΆ
Simulating coalescent histories in which some of the samples are not
from the present time is straightforward in msprime
.
By using the samples
argument to msprime.simulate()
we can specify the location and time at which all samples are made.
def historical_samples_example():
samples = [
msprime.Sample(population=0, time=0),
msprime.Sample(0, 0), # Or, we can use positional arguments.
msprime.Sample(0, 1.0)
]
tree_seq = msprime.simulate(samples=samples)
tree = next(tree_seq.trees())
for u in range(tree_seq.get_num_nodes()):
print(u, tree.get_parent(u), tree.get_time(u), sep="\t")
In this example we create three samples, two taken at the present time
and one taken 1.0 generations in the past. There are a number of
different ways in which we can describe the samples using the
msprime.Sample
object (samples can be provided as plain tuples also
if more convenient). Running this example, we get:
>>> historical_samples_example()
0 3 0.0
1 3 0.0
2 4 1.0
3 4 0.502039955384
4 1 4.5595966593
Because nodes 0
and 1
were sampled at time 0, their times in the tree
are both 0. Node 2
was sampled at time 1.0, and so its time is recorded
as 1.0 in the tree.
ReplicationΒΆ
A common task for coalescent simulations is to check the accuracy of analytical
approximations to statistics of interest. To do this, we require many independent
replicates of a given simulation. msprime
provides a simple and efficient
API for replication: by providing the num_replicates
argument to the
simulate()
function, we can iterate over the replicates
in a straightforward manner. Here is an example where we compare the
analytical results for the number of segregating sites with simulations:
import msprime
import numpy as np
def segregating_sites_example(n, theta, num_replicates):
S = np.zeros(num_replicates)
replicates = msprime.simulate(
sample_size=n,
mutation_rate=theta / 4,
num_replicates=num_replicates)
for j, tree_sequence in enumerate(replicates):
S[j] = tree_sequence.get_num_mutations()
# Now, calculate the analytical predictions
S_mean_a = np.sum(1 / np.arange(1, n)) * theta
S_var_a = (
theta * np.sum(1 / np.arange(1, n)) +
theta**2 * np.sum(1 / np.arange(1, n)**2))
print(" mean variance")
print("Observed {}\t\t{}".format(np.mean(S), np.var(S)))
print("Analytical {:.5f}\t\t{:.5f}".format(S_mean_a, S_var_a))
Running this code, we get:
>>> segregating_sites_example(10, 5, 100000)
mean variance
Observed 14.12173 52.4695318071
Analytical 14.14484 52.63903
Note that in this example we did not provide a value for the Ne
argument to simulate()
. In this case the effective population
size defaults to 1, which can be useful for theoretical work. However,
it is essential to remember that all rates and times must still be
scaled by 4 to convert into the coalescent time scale.
Population structureΒΆ
Population structure in msprime
closely follows the model used in the
ms
simulator: we have \(N\) demes with an \(N\times N\)
matrix describing the migration rates between these subpopulations. The
sample sizes, population sizes and growth rates of all demes
can be specified independently. Migration rates are specified using
a migration matrix. Unlike ms
however, all times and rates are specified
in generations and all populations sizes are absolute (that is, not
multiples of \(N_e\)).
In the following example, we calculate the mean coalescence time for a pair of lineages sampled in different demes in a symmetric island model, and compare this with the analytical expectation.
import msprime
import numpy as np
def migration_example():
# M is the overall symmetric migration rate, and d is the number
# of demes.
M = 0.2
d = 3
# We rescale m into pergeneration values for msprime.
m = M / (4 * (d  1))
# Allocate the initial sample. Because we are interested in the
# between deme coalescence times, we choose one sample each
# from the first two demes.
population_configurations = [
msprime.PopulationConfiguration(sample_size=1),
msprime.PopulationConfiguration(sample_size=1),
msprime.PopulationConfiguration(sample_size=0)]
# Now we set up the migration matrix. Since this is a symmetric
# island model, we have the same rate of migration between all
# pairs of demes. Diagonal elements must be zero.
migration_matrix = [
[0, m, m],
[m, 0, m],
[m, m, 0]]
# We pass these values to the simulate function, and ask it
# to run the required number of replicates.
num_replicates = int(1e6)
replicates = msprime.simulate(
population_configurations=population_configurations,
migration_matrix=migration_matrix,
num_replicates=num_replicates)
# And then iterate over these replicates
T = np.zeros(num_replicates)
for i, tree_sequence in enumerate(replicates):
tree = next(tree_sequence.trees())
# Convert the TMRCA to coalecent units.
T[i] = tree.get_time(tree.get_root()) / 4
# Finally, calculate the analytical expectation and print
# out the results
analytical = d / 2 + (d  1) / (2 * M)
print("Observed =", np.mean(T))
print("Predicted =", analytical)
Running this example we get:
>>> migration_example()
Observed = 6.50638181614
Predicted = 6.5
DemographyΒΆ
Msprime provides a flexible and simple way to model past demographic events in arbitrary combinations. Here is an example describing the Gutenkunst et al. outofAfrica model. See Figure 2B for a schematic of this model, and Table 1 for the values used.
Todo
Add a diagram of the model for convenience.
import math
def out_of_africa():
# First we set out the maximum likelihood values of the various parameters
# given in Table 1.
N_A = 7300
N_B = 2100
N_AF = 12300
N_EU0 = 1000
N_AS0 = 510
# Times are provided in years, so we convert into generations.
generation_time = 25
T_AF = 220e3 / generation_time
T_B = 140e3 / generation_time
T_EU_AS = 21.2e3 / generation_time
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two populations
r_EU = 0.004
r_AS = 0.0055
N_EU = N_EU0 / math.exp(r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(r_AS * T_EU_AS)
# Migration rates during the various epochs.
m_AF_B = 25e5
m_AF_EU = 3e5
m_AF_AS = 1.9e5
m_EU_AS = 9.6e5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
population_configurations = [
msprime.PopulationConfiguration(
sample_size=0, initial_size=N_AF),
msprime.PopulationConfiguration(
sample_size=1, initial_size=N_EU, growth_rate=r_EU),
msprime.PopulationConfiguration(
sample_size=1, initial_size=N_AS, growth_rate=r_AS)
]
migration_matrix = [
[ 0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(
time=T_EU_AS, source=2, destination=1, proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(0, 1)),
msprime.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(1, 0)),
msprime.PopulationParametersChange(
time=T_EU_AS, initial_size=N_B, growth_rate=0, population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(
time=T_B, source=1, destination=0, proportion=1.0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(
time=T_AF, initial_size=N_A, population_id=0)
]
# Use the demography debugger to print out the demographic history
# that we have just described.
dp = msprime.DemographyDebugger(
Ne=N_A,
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events)
dp.print_history()
The DemographyDebugger
provides a method to debug the history that
you have described so that you can be sure that the migration rates, population
sizes and growth rates are all as you intend during each epoch:
=============================
Epoch: 0  848.0 generations
=============================
start end growth_rate  0 1 2
      
0 1.23e+04 1.23e+04 0  0 3e05 1.9e05
1 2.97e+04 1e+03 0.004  3e05 0 9.6e05
2 5.41e+04 510 0.0055  1.9e05 9.6e05 0
Events @ generation 848.0
 Mass migration: lineages move from 2 to 1 with probability 1.0
 Migration rate change to 0 everywhere
 Migration rate change for (0, 1) to 0.00025
 Migration rate change for (1, 0) to 0.00025
 Population parameter change for 1: initial_size > 2100 growth_rate > 0
==================================
Epoch: 848.0  5600.0 generations
==================================
start end growth_rate  0 1 2
      
0 1.23e+04 1.23e+04 0  0 0.00025 0
1  2.1e+03 2.1e+03 0  0.00025 0 0
2 5.41e+04 2.41e07 0.0055  0 0 0
Events @ generation 5600.0
 Mass migration: lineages move from 1 to 0 with probability 1.0
===================================
Epoch: 5600.0  8800.0 generations
===================================
start end growth_rate  0 1 2
      
0 1.23e+04 1.23e+04 0  0 0.00025 0
1  2.1e+03 2.1e+03 0  0.00025 0 0
2 5.41e+04 0.00123 0.0055  0 0 0
Events @ generation 8800.0
 Population parameter change for 0: initial_size > 7300
================================
Epoch: 8800.0  inf generations
================================
start end growth_rate  0 1 2
      
0  7.3e+03 7.3e+03 0  0 0.00025 0
1  2.1e+03 2.1e+03 0  0.00025 0 0
2 5.41e+04 0 0.0055  0 0 0
Warning
The output of the DemographyDebugger.print_history()
method
is intended only for debugging purposes, and is not meant to be machine
readable. The format is also preliminary; if there is other information
that you think would be useful, please open an issue on GitHub
Once you are satisfied that the demographic history that you have built
is correct, it can then be simulated by calling the simulate()
function.
Recombination mapsΒΆ
The msprime
API allows us to quickly and easily simulate data from an
arbitrary recombination map. In this example we read a recombination
map for human chromosome 22, and simulate a single replicate. After
the simulation is completed, we plot histograms of the recombination
rates and the simulated breakpoints. These show that density of
breakpoints follows the recombination rate closely.
import numpy as np
import scipy.stats
import matplotlib.pyplot as pyplot
def variable_recomb_example():
infile = "hapmap/genetic_map_GRCh37_chr22.txt"
# Read in the recombination map using the read_hapmap method,
recomb_map = msprime.RecombinationMap.read_hapmap(infile)
# Now we get the positions and rates from the recombination
# map and plot these using 500 bins.
positions = np.array(recomb_map.get_positions()[1:])
rates = np.array(recomb_map.get_rates()[1:])
num_bins = 500
v, bin_edges, _ = scipy.stats.binned_statistic(
positions, rates, bins=num_bins)
x = bin_edges[:1][np.logical_not(np.isnan(v))]
y = v[np.logical_not(np.isnan(v))]
fig, ax1 = pyplot.subplots(figsize=(16, 6))
ax1.plot(x, y, color="blue")
ax1.set_ylabel("Recombination rate")
ax1.set_xlabel("Chromosome position")
# Now we run the simulation for this map. We assume Ne=10^4
# and have a sample of 100 individuals
tree_sequence = msprime.simulate(
sample_size=100,
Ne=10**4,
recombination_map=recomb_map)
# Now plot the density of breakpoints along the chromosome
breakpoints = np.array(list(tree_sequence.breakpoints()))
ax2 = ax1.twinx()
v, bin_edges = np.histogram(breakpoints, num_bins, density=True)
ax2.plot(bin_edges[:1], v, color="green")
ax2.set_ylabel("Breakpoint density")
ax2.set_xlim(1.5e7, 5.3e7)
fig.savefig("hapmap_chr22.svg")
Calculating LDΒΆ
The msprime
API provides methods to efficiently calculate
population genetics statistics. For example, the LdCalculator
class allows us to compute pairwise linkage disequilibrium coefficients.
Here we use the get_r2_matrix()
method to easily make an
LD plot using matplotlib. (Thanks to
the excellent scikitallel
for the basic plotting code
used here.)
import msprime
import matplotlib.pyplot as pyplot
def ld_matrix_example():
ts = msprime.simulate(100, recombination_rate=10, mutation_rate=20,
random_seed=1)
ld_calc = msprime.LdCalculator(ts)
A = ld_calc.get_r2_matrix()
# Now plot this matrix.
x = A.shape[0] / pyplot.rcParams['savefig.dpi']
x = max(x, pyplot.rcParams['figure.figsize'][0])
fig, ax = pyplot.subplots(figsize=(x, x))
fig.tight_layout(pad=0)
im = ax.imshow(A, interpolation="none", vmin=0, vmax=1, cmap="Blues")
ax.set_xticks([])
ax.set_yticks([])
for s in 'top', 'bottom', 'left', 'right':
ax.spines[s].set_visible(False)
pyplot.gcf().colorbar(im, shrink=.5, pad=0)
pyplot.savefig("ld.svg")
Working with threadsΒΆ
When performing large calculations it’s often useful to split the
work over multiple processes or threads. The msprime API can
be used without issues across multiple processes, and the Python
multiprocessing
module often provides a very effective way to
work with many replicate simulations in parallel.
When we wish to work with a single very large dataset, however, threads can
offer better resource usage because of the shared memory space. The Python
threading
library gives a very simple interface to lightweight CPU
threads and allows us to perform several CPU intensive tasks in parallel. The
msprime
API is designed to allow multiple threads to work in parallel when
CPU intensive tasks are being undertaken.
Note
In the CPython implementation the Global Interpreter Lock ensures that
only one thread executes Python bytecode at one time. This means that
Python code does not parallelise well across threads, but avoids a large
number of nasty pitfalls associated with multiple threads updating
data structures in parallel. Native C extensions like numpy
and msprime
release the GIL while expensive tasks are being performed, therefore
allowing these calculations to proceed in parallel.
In the following example we wish to find all mutations that are in approximate
LD (\(r^2 \geq 0.5\)) with a given set of mutations. We parallelise this
by splitting the input array between a number of threads, and use the
LdCalculator.get_r2_array()
method to compute the \(r^2\) value
both up and downstream of each focal mutation, filter out those that
exceed our threshold, and store the results in a dictionary. We also
use the very cool tqdm module to give us a
progress bar on this computation.
import threading
import numpy as np
import tqdm
import msprime
def find_ld_sites(
tree_sequence, focal_mutations, max_distance=1e6, r2_threshold=0.5,
num_threads=8):
results = {}
progress_bar = tqdm.tqdm(total=len(focal_mutations))
num_threads = min(num_threads, len(focal_mutations))
def thread_worker(thread_index):
ld_calc = msprime.LdCalculator(tree_sequence)
chunk_size = int(math.ceil(len(focal_mutations) / num_threads))
start = thread_index * chunk_size
for focal_mutation in focal_mutations[start: start + chunk_size]:
a = ld_calc.get_r2_array(
focal_mutation, max_distance=max_distance,
direction=msprime.REVERSE)
rev_indexes = focal_mutation  np.nonzero(a >= r2_threshold)[0]  1
a = ld_calc.get_r2_array(
focal_mutation, max_distance=max_distance,
direction=msprime.FORWARD)
fwd_indexes = focal_mutation + np.nonzero(a >= r2_threshold)[0] + 1
indexes = np.concatenate((rev_indexes[::1], fwd_indexes))
results[focal_mutation] = indexes
progress_bar.update()
threads = [
threading.Thread(target=thread_worker, args=(j,))
for j in range(num_threads)]
for t in threads:
t.start()
for t in threads:
t.join()
progress_bar.close()
return results
def threads_example():
ts = msprime.simulate(
sample_size=1000, Ne=1e4, length=1e7, recombination_rate=2e8,
mutation_rate=2e8)
counts = np.zeros(ts.get_num_mutations())
for t in ts.trees():
for mutation in t.mutations():
counts[mutation.index] = t.get_num_leaves(mutation.node)
doubletons = np.nonzero(counts == 2)[0]
results = find_ld_sites(ts, doubletons, num_threads=8)
print(
"Found LD sites for", len(results), "doubleton mutations out of",
ts.get_num_mutations())
In this example, we first simulate 1000 samples of 10 megabases and find all
doubleton mutations in the resulting tree sequence. We then call the
find_ld_sites()
function to find all mutations that are within 1 megabase
of these doubletons and have an \(r^2\) statistic of greater than 0.5.
The find_ld_sites()
function performs these calculations in parallel using
8 threads. The real work is done in the nested thread_worker()
function,
which is called once by each thread. In the thread worker, we first allocate an
instance of the LdCalculator
class. (It is critically important
that each thread has its own instance of LdCalculator
, as the threads
will not work efficiently otherwise.) After this, each thread works out the
slice of the input array that it is responsible for, and then iterates over
each focal mutation in turn. After the \(r^2\) values have been calculated,
we then find the indexes of the mutations corresponding to values greater than
0.5 using numpy.nonzero()
. Finally, the thread stores the resulting array
of mutation indexes in the results
dictionary, and moves on to the next
focal mutation.
Running this example we get:
>>> threads_example()
100%ββββββββββββββββββββββββββββββββββββββββββββββββ 4045/4045 [00:09<00:00, 440.29it/s]
Found LD sites for 4045 doubleton mutations out of 60100
API DocumentationΒΆ
This is the API documentation for msprime
, and provides detailed information
on the Python programming interface. See the Tutorial for an
introduction to using this API to run simulations and analyse the results.
Simulation modelΒΆ
The simulation model in msprime
closely follows the classical ms
program. Unlike ms
, however, time is measured in generations rather than
“coalescent units”. Internally the same simulation algorithm is used, but
msprime
provides a translation layer to allow the user input times and
rates in generations. Similarly, the times associated with the trees produced
by msprime
are in measured generations. To enable this translation from
generations into coalescent units and viceversa, a reference effective
population size must be provided, which is given by the Ne
parameter in the
simulate()
function. (Note that we assume diploid population sizes
thoughout, since we scale by \(4 N_e\).) Population sizes for individual
demes and for past demographic events are defined as absolute values, not
scaled by Ne
. All migration rates and growth rates are also per generation.
When running simulations we define the length in bases \(L\) of the
sequence in question using the length
parameter. This defines the
coordinate space within which trees and mutations are defined. \(L\) is a
continuous value, and coordinates can take any value from \(0\) to
\(L\). Mutations occur in an infinite sites process along this sequence,
and mutation rates are specified per generation, per unit of sequence length.
Thus, given the pergeneration mutation rate \(\mu\), the rate of mutation
over the entire sequence in coalescent time units is \(\theta = 4 N_e \mu
L\). It is important to remember these scaling factors when comparing with
analytical results!
Similarly, recombination rates are per base, per generation in msprime
.
Thus, given the per generation crossover rate \(r\), the overall rate
of recombination between the ends of the sequence in coalescent time units
is \(\rho = 4 N_e r L\). Recombination events occur in a continuous
coordinate space, so that breakpoints do not necessarily occur at integer
locations. However, the underlying recombination model is finite, and the
behaviour of a small number of loci can be modelled using the
RecombinationMap
class. However, this is considered an advanced
feature and the majority of cases should be well served with the default
recombination model and number of loci.
Population structure is modelled by specifying a fixed number of demes \(d\), and a \(d \times d\) matrix \(M\) of per generation migration rates. Each element of the matrix \(M_{j,k}\) defines the fraction of population \(j\) that consists of migrants from population \(k\) in each generation. Each deme has an initial absolute population size \(s\) and a per generation exponential growth rate \(\alpha\). The size of a given population at time \(t\) in the past (measured in generations) is therefore given by \(s e^{\alpha t}\). Demographic events that occur in the history of the simulated population alter some aspect of this population configuration at a particular time in the past.
Warning
This parameterisation of recombination, mutation and
migration rates is different to ms, which states these
rates over the entire region and in coalescent time units. The
motivation for this is to allow the user change the size of the simulated
region without having to rescale the recombination and mutation rates,
and to also allow users directly state times and rates in units of
generations. However, the mspms
command line application is
fully ms compatible.
Running simulationsΒΆ
The simulate()
function provides the primary interface to running
coalescent simulations in msprime.

msprime.
simulate
(sample_size=None, Ne=1, length=None, recombination_rate=None, recombination_map=None, mutation_rate=None, population_configurations=None, migration_matrix=None, demographic_events=[], samples=None, model=None, record_migrations=False, random_seed=None, mutation_generator=None, num_replicates=None)ΒΆ Simulates the coalescent with recombination under the specified model parameters and returns the resulting
TreeSequence
.Parameters:  sample_size (int) – The number of individuals in our sample.
If not specified or None, this defaults to the sum of the
subpopulation sample sizes. Either
sample_size
,population_configurations
orsamples
must be specified.  Ne (float) – The effective (diploid) population size for the reference population. This determines the factor by which the pergeneration recombination and mutation rates are scaled in the simulation. This defaults to 1 if not specified.
 length (float) – The length of the simulated region in bases.
This parameter cannot be used along with
recombination_map
. Defaults to 1 if not specified.  recombination_rate (float) – The rate of recombination per base
per generation. This parameter cannot be used along with
recombination_map
. Defaults to 0 if not specified.  recombination_map (
RecombinationMap
) – The map describing the changing rates of recombination along the simulated chromosome. This parameter cannot be used along with therecombination_rate
orlength
parameters, as these values are encoded within the map. Defaults to a uniform rate as described in therecombination_rate
parameter if not specified.  mutation_rate (float) – The rate of mutation per base per generation. If not specified, no mutations are generated.
 population_configurations (list or None.) – The list of
PopulationConfiguration
instances describing the sampling configuration, relative sizes and growth rates of the populations to be simulated. If this is not specified, a single population with a sample of sizesample_size
is assumed.  migration_matrix (list) – The matrix describing the rates
of migration between all pairs of populations. If \(N\)
populations are defined in the
population_configurations
parameter, then the migration matrix must be an \(N\times N\) matrix consisting of \(N\) lists of length \(N\).  demographic_events (list) – The list of demographic events to simulate. Demographic events describe changes to the populations in the past. Events should be supplied in nondecreasing order of time. Events with the same time value will be applied sequentially in the order that they were supplied before the simulation algorithm continues with the next time step.
 samples (list) – The list specifying the location and time of
all samples. This parameter may be used to specify historical
samples, and cannot be used in conjunction with the
sample_size
parameter. Each sample is a (population_id
,time
) pair such that the sample in positionj
in the list of samples is drawn in the specified population at the specfied time. Time is measured in generations, as elsewhere.  random_seed (int) – The random seed. If this is None, a random seed will be automatically generated. Valid random seeds must be between 1 and \(2^{32}  1\).
 num_replicates (int) – The number of replicates of the specified
parameters to simulate. If this is not specified or None,
no replication is performed and a
TreeSequence
object returned. Ifnum_replicates
is provided, the specified number of replicates is performed, and an iterator over the resultingTreeSequence
objects returned.
Returns: The
TreeSequence
object representing the results of the simulation if no replication is performed, or an iterator over the independent replicates simulated if thenum_replicates
parameter has been used.Return type: TreeSequence
or an iterator overTreeSequence
replicates.Warning: If using replication, do not store the results of the iterator in a list! For performance reasons, the same underlying object may be used for every TreeSequence returned which will most likely lead to unexpected behaviour.
 sample_size (int) – The number of individuals in our sample.
If not specified or None, this defaults to the sum of the
subpopulation sample sizes. Either
Population structureΒΆ
Population structure is modelled in msprime
by specifying a fixed number of
demes, with the migration rates between those demes defined by a migration
matrix. Each deme has an initial_size
that defines its absolute size at
time zero and a pergeneration growth_rate
which specifies the exponential
growth rate of the subpopulation. We must also define the size of the sample
to draw from each deme. The number of populations and their initial
configuration is defined using the population_configurations
parameter to
simulate()
, which takes a list of PopulationConfiguration
instances. Population IDs are zero indexed, and correspond to their position in
the list.
Samples are drawn sequentially from populations in increasing order of population ID. For example, if we specified an overall sample size of 5, and specify that 2 samples are drawn from population 0 and 3 from population 1, then individuals 0 and 1 will be initially located in population 0, and individuals 2, 3 and 4 will be drawn from population 2.
Given \(N\) populations, migration matrices are specified using an \(N
\times N\) matrix of demetodeme migration rates. See the documentation for
simulate()
and the Simulation model section for more details on the
migration rates.

class
msprime.
PopulationConfiguration
(sample_size=None, initial_size=None, growth_rate=0.0)ΒΆ The initial configuration of a population (or deme) in a simulation.
Parameters:  sample_size (int) – The number of initial samples that are drawn from this population.
 initial_size (float) – The absolute size of the population at time zero. Defaults to the reference population size \(N_e\).
 growth_rate (float) – The exponential growth rate of the population per generation. Growth rates can be negative. This is zero for a constant population size. Defaults to 0.
Demographic EventsΒΆ
Demographic events change some aspect of the population configuration
at some time in the past, and are specified using the demographic_events
parameter to simulate()
. Each element of this list must be an
instance of one of the following demographic events
that are currently supported. Note that all times are measured in
generations, all sizes are absolute (i.e., not relative to \(N_e\)),
and all rates are pergeneration.

class
msprime.
PopulationParametersChange
(time, initial_size=None, growth_rate=None, population_id=None)ΒΆ Changes the demographic parameters of a population at a given time.
This event generalises the
eg
,eG
,en
andeN
options fromms
. Note that unlikems
we do not automatically set growth rates to zero when the population size is changed.Parameters:  time (float) – The time at which this event occurs in generations.
 initial_size (float) – The absolute size of the population
at the beginning of the time slice starting at
time
. If None, this is calculated according to the initial population size and growth rate over the preceding time slice.  growth_rate (float) – The new pergeneration growth rate. If None, the growth rate is not changed. Defaults to None.
 population_id (int) – The ID of the population affected. If
population_id
is None, the changes affect all populations simultaneously.

class
msprime.
MigrationRateChange
(time, rate, matrix_index=None)ΒΆ Changes the rate of migration to a new value at a specific time.
Parameters:  time (float) – The time at which this event occurs in generations.
 rate (float) – The new pergeneration migration rate.
 matrix_index (tuple) – A tuple of two population IDs descibing
the matrix index of interest. If
matrix_index
is None, all nondiagonal entries of the migration matrix are changed simultaneously.

class
msprime.
MassMigration
(time, source, destination, proportion=1.0)ΒΆ A mass migration event in which some fraction of the population in one deme simultaneously move to another deme, viewed backwards in time. For each lineage currently present in the source population, they move to the destination population with probability equal to
proportion
.This event class generalises the population split (
ej
) and admixture (es
) events fromms
. Note that MassMigrations do not have any side effects on the migration matrix.Parameters:  time (float) – The time at which this event occurs in generations.
 source (int) – The ID of the source population.
 destination (int) – The ID of the destination population.
 proportion (float) – The probability that any given lineage within the source population migrates to the destination population.
Debugging demographic modelsΒΆ
Warning
The DemographyDebugger
class is preliminary, and the API
is likely to change in the future.

class
msprime.
DemographyDebugger
(Ne=1, population_configurations=None, migration_matrix=None, demographic_events=[])ΒΆ A class to facilitate debugging of population parameters and migration rates in the past.

print_history
(output=<open file '<stdout>', mode 'w'>)ΒΆ Prints a summary of the history of the populations.

Variable recombination ratesΒΆ

class
msprime.
RecombinationMap
(positions, rates, num_loci=None)ΒΆ A RecombinationMap represents the changing rates of recombination along a chromosome. This is defined via two lists of numbers:
positions
andrates
, which must be of the same length. Given an index j in these lists, the rate of recombination per base per generation isrates[j]
over the intervalpositions[j]
topositions[j + 1]
. Consequently, the first position must be zero, and by convention the last rate value is also required to be zero (although it does not used).Parameters:  positions (list) – The positions (in bases) denoting the distinct intervals where recombination rates change. These can be floating point values.
 rates (list) – The list of rates corresponding to the supplied
positions
. Recombination rates are specified per base, per generation.  num_loci (int) – The maximum number of nonrecombining loci in the underlying simulation. By default this is set to the largest possible value, allowing the maximum resolution in the recombination process. However, for a finite sites model this can be set to smaller values.

classmethod
read_hapmap
(filename)ΒΆ Parses the specified file in HapMap format. These files must contain a single header line (which is ignored), and then each subsequent line denotes a position/rate pair. Positions are in units of bases, and recombination rates in centimorgans/Megabase. The first column in this file is ignored, as are subsequence columns after the Position and Rate. A sample of this format is as follows:
Chromosome Position(bp) Rate(cM/Mb) Map(cM) chr1 55550 2.981822 0.000000 chr1 82571 2.082414 0.080572 chr1 88169 2.081358 0.092229 chr1 254996 3.354927 0.439456 chr1 564598 2.887498 1.478148
Parameters: filename (str) – The name of the file to be parsed. This may be in plain text or gzipped plain text.
Processing resultsΒΆ
The TreeSequence
class represents a sequence of correlated trees
output by a simulation. The SparseTree
class represents a single
tree in this sequence.

msprime.NULL_NODE = 1
Special reserved value, representing the null node. If the parent of a given node is null, then this node is a root. Similarly, if the children of a node are null, this node is a leaf.

msprime.NULL_POPULATION = 1
Special reserved value, representing the null population ID. If the population associated with a particular tree node is not defined, or population information was not available in the underlying tree sequence, then this value will be returned by
SparseTree.get_population()
.

msprime.FORWARD = 1
Constant representing the forward direction of travel (i.e., increasing coordinate values).

msprime.REVERSE = 1
Constant representing the reverse direction of travel (i.e., decreasing coordinate values).

msprime.
load
(path)ΒΆ Loads a tree sequence from the specified file path. This file must be in the HDF5 file format produced by the
TreeSequence.dump()
method.Parameters: path (str) – The file path of the HDF5 file containing the tree sequence we wish to load. Returns: The tree sequence object containing the information stored in the specified file path. Return type: msprime.TreeSequence

class
msprime.
TreeSequence
ΒΆ A TreeSequence represents the information generated in a coalescent simulation. This includes all the trees across the simulated region, along with the mutations (if any are present).

Y
(leaf_sets, windows)ΒΆ Finds the ‘Y’ statistic between the three groups of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap). If the leaf_sets are A, B, and C, then the result gives the mean total length of any edge in the tree between a and the most recent common ancestor of b and c, where a, b, and c are random draws from A, B, and C respectively.
Parameters:  leaf_sets (list) – A list of three sets of IDs of leaves: (A,B,C).
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of numeric values computed separately on each window.

Y_vector
(leaf_sets, windows, indices)ΒΆ Finds the ‘Y’ statistic between three leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap). If the leaf_sets are A, B, and C, then the result gives the mean total length of any edge in the tree between a and the most recent common ancestor of b and c, where a, b, and c are random draws from A, B, and C respectively.
 The result is, for each window, a vector whose kth entry is
 Y(leaf_sets[indices[k][0]], leaf_sets[indices[k][1]],
 leaf_sets[indices[k][2]]).
Parameters:  leaf_sets (list) – A list of three sets of IDs of leaves: (A,B,C).
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
 indices (list) – A list of triples of indices of leaf_sets.
Returns: A list of numeric vectors of length equal to the length of indices, computed separately on each window.

branch_stats
(leaf_sets, weight_fun)ΒΆ Here leaf_sets is a list of lists of leaves, and weight_fun is a function whose argument is a list of integers of the same length as leaf_sets that returns a boolean. A branch in a tree is weighted by weight_fun(x), where x[i] is the number of leaves in leaf_sets[i] below that branch. This finds the sum of all counted branches for each tree, and averages this across the tree sequence, weighted by genomic length.

branch_stats_vector
(leaf_sets, weight_fun, windows=None)ΒΆ Here leaf_sets is a list of lists of leaves, and weight_fun is a function whose argument is a list of integers of the same length as leaf_sets that returns a boolean. A branch in a tree is weighted by weight_fun(x), where x[i] is the number of leaves in leaf_sets[i] below that branch. This finds the sum of all counted branches for each tree, and averages this across the tree sequence, weighted by genomic length.
It does this separately for each window [windows[i], windows[i+1]) and returns the values in a list.

branch_stats_windowed
(leaf_sets, weight_fun, windows=None)ΒΆ Here leaf_sets is a list of lists of leaves, and weight_fun is a function whose argument is a list of integers of the same length as leaf_sets that returns a boolean. A branch in a tree is weighted by weight_fun(x), where x[i] is the number of leaves in leaf_sets[i] below that branch. This finds the sum of all counted branches for each tree, and averages this across the tree sequence, weighted by genomic length.

breakpoints
()ΒΆ Returns an iterator over the breakpoints along the chromosome, including the two extreme points 0 and L. This is equivalent to
>>> [0] + [t.get_interval()[1] for t in self.trees()]
although we do not build an explicit list.
Returns: An iterator over all the breakpoints along the simulated sequence. Return type: iter

diffs
()ΒΆ Returns an iterator over the differences between adjacent trees in this tree sequence. Each diff returned by this method is a tuple of the form (length, records_out, records_in). The length is the length of the genomic interval covered by the current tree, and is equivalent to the value returned by
msprime.SparseTree.get_length()
. The records_out value is list of \((u, c, t)\) tuples, and corresponds to the coalescence records that have been invalidated by moving to the current tree. As in therecords()
method, \(u\) is a tree node, \(c\) is a tuple containing its children, and \(t\) is the time the event occurred. These records are returned in timedecreasing order, such that the record affecting the highest parts of the tree (i.e., closest to the root) are returned first. The records_in value is also a list of \((u, c, t)\) tuples, and these describe the records that must be applied to create the tree covering the current interval. These records are returned in timeincreasing order, such that the records affecting the lowest parts of the tree (i.e., closest to the leaves) are returned first.Returns: An iterator over the diffs between adjacent trees in this tree sequence. Return type: iter

dump
(path, zlib_compression=False)ΒΆ Writes the tree sequence to the specified file path.
Parameters:

dump_samples_text
(samples, precision=6)ΒΆ Writes a text representation of the entries in the NodeTable corresponding to samples to the specified connections.
Parameters:  samples (stream) – The filelike object to write the subset of the NodeTable describing the samples to, with an extra column, id.
 precision (int) – The number of digits of precision.

dump_tables
(nodes=None, edgesets=None, migrations=None, sites=None, mutations=None)ΒΆ Copy the contents of the tables underlying the tree sequence to the specified objects.
Parameters:  nodes (NodeTable) – The NodeTable to load the nodes into.
 edgesets (EdgesetTable) – The EdgesetTable to load the edgesets into.
 migrations (MigrationTable) – The MigrationTable to load the migrations into.
 sites (SiteTable) – The SiteTable to load the sites into.
 mutations (MutationTable) – The NodeTable to load the mutations into.
Returns: A TableTuple containing all tables underlying the tree sequence.
Return type: TableTuple

dump_text
(nodes=None, edgesets=None, sites=None, mutations=None, precision=6)ΒΆ Writes a text representation of the tables underlying the tree sequence to the specified connections.
Parameters:  nodes (stream) – The filelike object (having a .write() method) to write the NodeTable to.
 edgesets (stream) – The filelike object to write the EdgesetTable to.
 sites (stream) – The filelike object to write the SiteTable to.
 mutations (stream) – The filelike object to write the MutationTable to.
 precision (int) – The number of digits of precision.

f2
(leaf_sets, windows)ΒΆ Finds the Patterson’s f2 statistics between the three groups of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
f2(A;B) is f4(A,B;A,B) corrected to not include self comparisons.
Parameters:  leaf_sets (list) – A list of two sets of IDs of leaves: (A,B), each having at least two samples.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of values of f2(A,B) computed separately on each window.

f2_vector
(leaf_sets, windows, indices)ΒΆ Finds the Patterson’s f2 statistics between multiple subsets of pairs of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
f2(A;B) is f4(A,B;A,B) corrected to not include self comparisons.
Parameters:  leaf_sets (list) – A list of sets of IDs of leaves, each having at least two samples.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
 indices (list) – A list of pairs of indices of leaf_sets.
Returns: A list of values of f2(A,C) computed separately on each window.

f3
(leaf_sets, windows)ΒΆ Finds the Patterson’s f3 statistics between the three groups of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
f3(A;B,C) is f4(A,B;A,C) corrected to not include self comparisons.
Parameters:  leaf_sets (list) – A list of three sets of IDs of leaves: (A,B,C), with the first set having at least two samples.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of values of f3(A,B,C) computed separately on each window.

f3_vector
(leaf_sets, windows, indices)ΒΆ Finds the Patterson’s f3 statistics between multiple subsets of three groups of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
f3(A;B,C) is f4(A,B;A,C) corrected to not include self comparisons.
If A does not contain at least three samples, the result is NaN.
Parameters:  leaf_sets (list) – A list of sets of IDs of leaves.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
 indices (list) – A list of triples of indices of leaf_sets.
Returns: A list of values of f3(A,B,C) computed separately on each window.

f4
(leaf_sets, windows)ΒΆ Finds the Patterson’s f4 statistics between the four groups of leaves in leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
Parameters:  leaf_sets (list) – A list of four sets of IDs of leaves: (A,B,C,D)
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of values of f4(A,B;C,D) computed separately on each window.

f4_vector
(leaf_sets, windows, indices)ΒΆ Finds the Patterson’s f4 statistics between multiple subsets of four groups of leaf_sets. The leaf_sets should be disjoint (the computation works fine, but if not the result depends on the amount of overlap).
Parameters:  leaf_sets (list) – A list of four sets of IDs of leaves: (A,B,C,D)
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
 indices (list) – A list of 4tuples of indices of leaf_sets.
Returns: A list of values of f4(A,B;C,D) of length equal to the length of indices, computed separately on each window.

get_num_mutations
()ΒΆ Returns the number of mutations in this tree sequence. See the
msprime.TreeSequence.mutations()
method for details on how mutations are defined.Returns: The number of mutations in this tree sequence. Return type: int

get_num_nodes
()ΒΆ Returns the number of nodes in this tree sequence. This 1 + the largest value \(u\) such that u is a node in any of the constituent trees.
Returns: The total number of nodes in this tree sequence. Return type: int

get_num_records
()ΒΆ Returns the number of coalescence records in this tree sequence. See the
records()
method for details on these objects.Returns: The number of coalescence records defining this tree sequence. Return type: int

get_num_trees
()ΒΆ Returns the number of distinct trees in this tree sequence. This is equal to the number of trees returned by the
trees()
method.Returns: The number of trees in this tree sequence. Return type: int

get_pairwise_diversity
(samples=None)ΒΆ Returns the value of pi, the pairwise nucleotide site diversity, which is the average number of mutations that differ between a randomly chosen pair of samples. If samples is specified, calculate the diversity within this set.
Parameters: samples (iterable) – The set of samples within which we calculate the diversity. If None, calculate diversity within the entire sample. Returns: The pairwise nucleotide site diversity. Return type: float

get_population
(u)ΒΆ Returns the population ID for the specified sample ID.
Parameters: u (int) – The individual ID of interest. Returns: The population ID where the specified individual lived. Returns NULL_POPULATION
if no population information is available.Return type: int

get_sample_size
()ΒΆ Returns the sample size for this tree sequence. This is the number of leaf nodes in each tree.
Returns: The number of leaf nodes in the tree sequence. Return type: int

get_samples
(population_id=None)ΒΆ Returns the samples matching the specified population ID.
Parameters: population_id (int) – The population of interest. If None, return all samples. Returns: The ID of the population we wish to find samples from. If None, return samples from all populations. Return type: list

get_sequence_length
()ΒΆ Returns the sequence length in this tree sequence. This defines the genomic scale over which tree coordinates are defined. Given a tree sequence with a sequence length \(L\), the constituent trees will be defined over the halfclosed interval \((0, L]\). Each tree then covers some subset of this interval — see
msprime.SparseTree.get_interval()
for details.Returns: The length of the sequence in this tree sequence in bases. Return type: float

get_time
(u)ΒΆ Returns the time that the specified ID was alive at.
Parameters: u (int) – The individual ID of interest. Returns: The time at which the specified individual was alive at. Return type: int

haplotypes
()ΒΆ Returns an iterator over the haplotypes resulting from the trees and mutations in this tree sequence as a string of ‘1’s and ‘0’s. The iterator returns a total of \(n\) strings, each of which contains \(s\) characters (\(n\) is the sample size returned by
msprime.TreeSequence.get_sample_size()
and \(s\) is the number of mutations returned bymsprime.TreeSequence.get_num_mutations()
). The first string returned is the haplotype for sample 0, and so on.Returns: An iterator over the haplotype strings for the samples in this tree sequence. Return type: iter

mean_pairwise_tmrca
(leaf_sets, windows)ΒΆ Finds the mean time to most recent common ancestor between pairs of samples as described in mean_pairwise_tmrca_matrix (which uses this function). Returns the upper triangle (including the diagonal) in rowmajor order, so if the output is x, then:
>>> k=0 >>> for w in range(len(windows)1): >>> for i in range(len(leaf_sets)): >>> for j in range(i,len(leaf_sets)): >>> trmca[i,j] = tmrca[j,i] = x[w][k] >>> k += 1
will fill out the matrix of mean TMRCAs in the i`th window between (and within) each group of leaves in `leaf_sets in the matrix tmrca. Alternatively, if names labels the leaf_sets, the output labels are:
>>> [".".join(names[i],names[j]) for i in range(len(names)) >>> for j in range(i,len(names))]
Parameters:  leaf_sets (list) – A list of sets of IDs of leaves.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of the upper triangle of mean TMRCA values in rowmajor order, including the diagonal.

mean_pairwise_tmrca_matrix
(leaf_sets, windows)ΒΆ Finds the mean time to most recent common ancestor between pairs of samples from each set of leaves and in each window. Returns a numpy array indexed by (window, leaf_set, leaf_set). Diagonal entries are corrected so that the value gives the mean pairwise TMRCA for distinct samples, but it is not checked whether the leaf_sets are disjoint (so offdiagonals are not corrected). For this reason, if an element of leaf_sets has only one element, the corresponding diagonal will be NaN.
The mean TMRCA between two samples is defined to be onehalf the length of all edges separating them in the tree at a uniformly chosen position on the genome.
Parameters:  leaf_sets (list) – A list of sets of IDs of leaves.
 windows (iterable) – The breakpoints of the windows (including start and end, so has one more entry than number of windows).
Returns: A list of the upper triangle of mean TMRCA values in rowmajor order, including the diagonal.

mutations
()ΒΆ Returns an iterator over the mutations in this tree sequence. Each mutation is represented as a tuple \((x, u, j)\) where \(x\) is the position of the mutation in the sequence in chromosome coordinates, \(u\) is the node over which the mutation occurred and \(j\) is the zerobased index of the mutation within the overall tree sequence. Mutations are returned in nondecreasing order of position and increasing index.
Each mutation returned is an instance of
collections.namedtuple()
, and may be accessed via the attributesposition
,node
andindex
as well as the usual positional approach. This is the recommended interface for working with mutations as it is both more readable and also ensures that code is forward compatible with future extensions.Returns: An iterator of all \((x, u, j)\) tuples defining the mutations in this tree sequence. Return type: iter

records
()ΒΆ Returns an iterator over the coalescence records in this tree sequence in timesorted order. Each record is a tuple \((l, r, u, c, t, d)\) defining the assignment of a tree node across an interval. The range of this record is the halfopen genomic interval \([l, r)\), such that it applies to all positions \(l \leq x < r\). Each record represents the assignment of a pair of children \(c\) to a parent parent \(u\). This assignment happens at \(t\) generations in the past within the population with ID \(d\). If population information was not stored for this tree sequence then the population ID will be
NULL_POPULATION
.Each record returned is an instance of
collections.namedtuple()
, and may be accessed via the attributesleft
,right
,node
,children
,time
andpopulation
, as well as the usual positional approach. For example, if we wished to print out the genomic length of each record, we could write:>>> for record in tree_sequence.records(): >>> print(record.right  record.left)
Returns: An iterator of all \((l, r, u, c, t, d)\) tuples defining the coalescence records in this tree sequence. Return type: iter

trees
(tracked_leaves=None, leaf_counts=True, leaf_lists=False)ΒΆ Returns an iterator over the trees in this tree sequence. Each value returned in this iterator is an instance of
SparseTree
.The
leaf_counts
andleaf_lists
parameters control the features that are enabled for the resulting trees. Ifleaf_counts
is True, then it is possible to count the number of leaves underneath a particular node in constant time using theget_num_leaves()
method. Ifleaf_lists
is True a more efficient algorithm is used in theSparseTree.leaves()
method.The
tracked_leaves
parameter can be used to efficiently count the number of leaves in a given set that exist in a particular subtree using theSparseTree.get_num_tracked_leaves()
method. It is an error to use thetracked_leaves
parameter when theleaf_counts
flag is False.Warning: Do not store the results of this iterator in a list! For performance reasons, the same underlying object is used for every tree returned which will most likely lead to unexpected behaviour.
Parameters:  tracked_leaves (list) – The list of leaves to be tracked and
counted using the
SparseTree.get_num_tracked_leaves()
method.  leaf_counts (bool) – If True, support constant time leaf counts
via the
SparseTree.get_num_leaves()
andSparseTree.get_num_tracked_leaves()
methods.  leaf_lists (bool) – If True, provide more efficient access
to the leaves beneath a give node using the
SparseTree.leaves()
method.
Returns: An iterator over the sparse trees in this tree sequence.
Return type:  tracked_leaves (list) – The list of leaves to be tracked and
counted using the

variants
(as_bytes=False)ΒΆ Returns an iterator over the variants in this tree sequence. Each variant corresponds to a single mutation and is represented as a tuple \((x, u, j, g)\). The values of \(x\), \(u\) and \(j\) are identical to the values returned by the
TreeSequence.mutations()
method, and \(g\) represents the sample genotypes for this variant. Thus, \(g[k]\) is the observed state for sample \(k\) at this site; zero represents the ancestral type and one the derived type.Each variant returned is an instance of
collections.namedtuple()
, and may be accessed via the attributesposition
,node
,index
andgenotypes
as well as the usual positional approach. This is the recommended interface for working with variants as it is both more readable and also ensures that code is forward compatible with future extensions.The returned genotypes may be either a numpy array of 1 byte unsigned integer 0/1 values, or a Python bytes object of ‘0’/‘1’ ASCII characters. This behaviour is controller by the
as_bytes
parameter. The default behaviour is to return a numpy array, which is substantially more efficient.Warning: The same numpy array is used to represent genotypes between iterations, so if you wish the store the results of this iterator you must take a copy of the array. This warning does not apply when as_bytes
is True, as a new bytes object is allocated for each variant.Parameters: as_bytes (bool) – If True, the genotype values will be returned as a Python bytes object. This is useful in certain situations (i.e., directly printing the genotypes) or when numpy is not available. Otherwise, genotypes are returned as a numpy array (the default). Returns: An iterator of all \((x, u, j, g)\) tuples defining the variants in this tree sequence.

write_vcf
(output, ploidy=1, contig_id='1')ΒΆ Writes a VCF formatted file to the specified filelike object. If a ploidy value is supplied, allele values are combined among adjacent samples to form a phased genotype of the required ploidy. For example, if we have a ploidy of 2 and a sample of size 6, then we will have 3 diploid samples in the output, consisting of the combined alleles for samples [0, 1], [2, 3] and [4, 5]. If we had alleles 011110 at a particular variant, then we would output the genotypes 01, 11 and 10 in VCF. Sample names are generated by appending the index to the prefix
msp_
such that we would have the sample namesmsp_0
,msp_1
andmsp_2
in the running example.Example usage:
>>> with open("output.vcf", "w") as vcf_file: >>> tree_sequence.write_vcf(vcf_file, 2)
Parameters:


class
msprime.
SparseTree
ΒΆ A SparseTree is a single tree in a
TreeSequence
. In a sparse tree for a sample of size \(n\), the leaves are nodes \(0\) to \(n  1\) inclusive and internal nodes are integers \(\geq n\). The value of these nodes is strictly increasing as we ascend the tree and the root of the tree is the node with the largest value that is reachable from the leaves. Each node in the tree has a parent which is obtained using theget_parent()
method. The parent of the root node is theNULL_NODE
, \(1\). Similarly, each internal node has a pair of children, which are obtained using theget_children()
method. Each node in the tree has a time associated with it in generations. This value is obtained using theSparseTree.get_time()
method.Sparse trees are not intended to be instantiated directly, and are obtained as part of a
TreeSequence
using thetrees()
method.
draw
(path=None, width=200, height=200, times=False, mutation_locations=True, mutation_labels=False, internal_node_labels=True, leaf_node_labels=True, show_times=None)ΒΆ Returns a representation of this tree in SVG format.
Parameters:  path (str) – The path to the file to write the SVG. If None, do not write to file.
 width (int) – The width of the image in pixels.
 height (int) – The height of the image in pixels.
 times (bool) – If True, show time labels at each internal node.
 mutation_locations (bool) – If True, show mutations as points over nodes.
 mutation_labels (bool) – If True, show labels for mutations.
 internal_node_labels (bool) – If True, show labels for internal nodes.
 leaf_node_labels (bool) – If True, show labels for leaf nodes.
 show_times (bool) – Deprecated alias for
times
.
Returns: A representation of this tree in SVG format.
Return type:

get_branch_length
(u)ΒΆ Returns the length of the branch (in generations) joining the specified node to its parent. This is equivalent to
>>> tree.get_time(tree.get_parent(u))  tree.get_time(u)
Note that this is not related to the value returned by
get_length()
, which describes the length of the interval covered by the tree in genomic coordinates.Parameters: u (int) – The node of interest. Returns: The branch length from u to its parent. Return type: float

get_children
(u)ΒΆ Returns the children of the specified node as a tuple \((v, w)\). For internal nodes, this tuple is always in sorted order such that \(v < w\). If u is a leaf or is not a node in the current tree, return the tuple (
NULL_NODE
,NULL_NODE
).Parameters: u (int) – The node of interest. Returns: The children of u as a pair of integers Return type: tuple

get_index
()ΒΆ Returns the index this tree occupies in the parent tree sequence. This index is zero based, so the first tree in the sequence has index 0.
Returns: The index of this tree. Return type: int

get_interval
()ΒΆ Returns the coordinates of the genomic interval that this tree represents the history of. The interval is returned as a tuple \((l, r)\) and is a halfopen interval such that the left coordinate is inclusive and the right coordinate is exclusive. This tree therefore applies to all genomic locations \(x\) such that \(l \leq x < r\).
Returns: A tuple (l, r) representing the leftmost (inclusive) and rightmost (exclusive) coordinates of the genomic region covered by this tree. Return type: tuple

get_length
()ΒΆ Returns the length of the genomic interval that this tree represents. This is defined as \(r  l\), where \((l, r)\) is the genomic interval returned by
get_interval()
.Returns: The length of the genomic interval covered by this tree. Return type: int

get_mrca
(u, v)ΒΆ Returns the most recent common ancestor of the specified nodes.
Parameters: Returns: The most recent common ancestor of u and v.
Return type:

get_num_leaves
(u)ΒΆ Returns the number of leaves in this tree underneath the specified node.
If the
TreeSequence.trees()
method is called withleaf_counts=True
this method is a constant time operation. If not, a slower traversal based algorithm is used to count the leaves.Parameters: u (int) – The node of interest. Returns: The number of leaves in the subtree rooted at u. Return type: int

get_num_mutations
()ΒΆ Returns the number of mutations on this tree.
Returns: The number of mutations on this tree. Return type: int

get_num_tracked_leaves
(u)ΒΆ Returns the number of leaves in the set specified in the
tracked_leaves
parameter of theTreeSequence.trees()
method underneath the specified node. This is a constant time operation.Parameters: u (int) – The node of interest. Returns: The number of leaves within the set of tracked leaves in the subtree rooted at u. Return type: int Raises: RuntimeError – if the TreeSequence.trees()
method is not called withleaf_counts=True
.

get_parent
(u)ΒΆ Returns the parent of the specified node. Returns the
NULL_NODE
1 if u is the root or is not a node in the current tree.Parameters: u (int) – The node of interest. Returns: The parent of u. Return type: int

get_population
(u)ΒΆ Returns the population associated with the specified node. For leaf nodes this is the population of the sample, and for internal nodes this is the population where the corresponding coalescence occured. If the specified node is not a member of this tree or population level information was not stored in the tree sequence,
NULL_POPULATION
is returned.Parameters: u (int) – The node of interest. Returns: The ID of the population associated with node u. Return type: int

get_sample_size
()ΒΆ Returns the sample size for this tree. This is the number of leaf nodes in the tree.
Returns: The number of leaf nodes in the tree. Return type: int

get_time
(u)ΒΆ Returns the time of the specified node in generations. Returns 0 if u is a leaf or is not a node in the current tree.
Parameters: u (int) – The node of interest. Returns: The time of u. Return type: float

get_tmrca
(u, v)ΒΆ Returns the time of the most recent common ancestor of the specified nodes. This is equivalent to:
tree.get_time(tree.get_mrca(u, v))
Parameters: Returns: The time of the most recent common ancestor of u and v.
Return type:

get_total_branch_length
()ΒΆ Returns the sum of all the branch lengths in this tree (in units of generations). This is equivalent to
>>> sum( >>> tree.get_branch_length(u) for u in tree.nodes() >>> if u != tree.get_root())
Returns: The sum of all the branch lengths in this tree.

is_internal
(u)ΒΆ Returns True if the specified node is not a leaf.
Parameters: u (int) – The node of interest. Returns: True if u is not a leaf node. Return type: bool

is_leaf
(u)ΒΆ Returns True if the specified node is a leaf. A node \(u\) is a leaf if it has zero children.
Parameters: u (int) – The node of interest. Returns: True if u is a leaf node. Return type: bool

leaves
(u)ΒΆ Returns an iterator over all the leaves in this tree underneath the specified node.
If the
TreeSequence.trees()
method is called withleaf_lists=True
, this method uses an efficient algorithm to find the leaves. If not, a simple traversal based method is used.Parameters: u (int) – The node of interest. Returns: An iterator over all leaves in the subtree rooted at u. Return type: iterator

mutations
()ΒΆ Returns an iterator over the mutations in this tree. Each mutation is represented as a tuple \((x, u, j)\) where \(x\) is the position of the mutation in the sequence in chromosome coordinates, \(u\) is the node over which the mutation occurred and \(j\) is the zerobased index of the mutation within the overall tree sequence. Mutations are returned in nondecreasing order of position and increasing index.
Each mutation returned is an instance of
collections.namedtuple()
, and may be accessed via the attributesposition
,node
andindex
as well as the usual positional approach. This is the recommended interface for working with mutations as it is both more readable and also ensures that code is forward compatible with future extensions.Returns: An iterator of all \((x, u, j)\) tuples defining the mutations in this tree. Return type: iter

nodes
(root=None, order='preorder')ΒΆ Returns an iterator over the nodes in this tree. If the root parameter is provided, iterate over the nodes in the subtree rooted at this node. If this is None, iterate over all nodes. If the order parameter is provided, iterate over the nodes in required tree traversal order.
Parameters: Return type: iterator

Calculating statisticsΒΆ
The msprime
API provides methods for efficiently calculating
population genetics statistics from a given TreeSequence
.

class
msprime.
LdCalculator
(tree_sequence)ΒΆ Class for calculating linkage disequilibrium coefficients between pairs of mutations in a
TreeSequence
. This class requires the numpy library.This class supports multithreaded access using the Python
threading
module. Separate instances ofLdCalculator
referencing the same tree sequence can operate in parallel in multiple threads. See the Working with threads section in the Tutorial for an example of how use multiple threads to calculate LD values efficiently.Parameters: tree_sequence (TreeSequence) – The tree sequence containing the mutations we are interested in. 
get_r2
(a, b)ΒΆ Returns the value of the \(r^2\) statistic between the pair of mutations at the specified indexes. This method is not an efficient method for computing large numbers of pairwise; please use either
get_r2_array()
orget_r2_matrix()
for this purpose.Parameters: Returns: The value of \(r^2\) between the mutations at indexes
a
andb
.Return type:

get_r2_array
(a, direction=1, max_mutations=None, max_distance=None)ΒΆ Returns the value of the \(r^2\) statistic between the focal mutation at index \(a\) and a set of other mutations. The method operates by starting at the focal mutation and iterating over adjacent mutations (in either the forward or backwards direction) until either a maximum number of other mutations have been considered (using the
max_mutations
parameter), a maximum distance in sequence coordinates has been reached (using themax_distance
parameter) or the start/end of the sequence has been reached. For every mutation \(b\) considered, we then insert the value of \(r^2\) between \(a\) and \(b\) at the corresponding index in an array, and return the entire array. If the returned array is \(x\) anddirection
ismsprime.FORWARD
then \(x[0]\) is the value of the statistic for \(a\) and \(a + 1\), \(x[1]\) the value for \(a\) and \(a + 2\), etc. Similarly, ifdirection
ismsprime.REVERSE
then \(x[0]\) is the value of the statistic for \(a\) and \(a  1\), \(x[1]\) the value for \(a\) and \(a  2\), etc.Parameters:  a (int) – The index of the focal mutation.
 direction (int) – The direction in which to travel when
examining other mutations. Must be either
msprime.FORWARD
ormsprime.REVERSE
. Defaults tomsprime.FORWARD
.  max_mutations (int) – The maximum number of mutations to return \(r^2\) values for. Defaults to as many mutations as possible.
 max_distance (float) – The maximum absolute distance between the focal mutation and those for which \(r^2\) values are returned.
Returns: An array of double precision floating point values representing the \(r^2\) values for mutations in the specified direction.
Return type: numpy.ndarray
Warning: For efficiency reasons, the underlying memory used to store the returned array is shared between calls. Therefore, if you wish to store the results of a single call to
get_r2_array()
for later processing you must take a copy of the array!

get_r2_matrix
()ΒΆ Returns the complete \(m \times m\) matrix of pairwise \(r^2\) values in a tree sequence with \(m\) mutations.
Returns: An 2 dimensional square array of double precision floating point values representing the \(r^2\) values for all pairs of mutations. Return type: numpy.ndarray

Command line interfaceΒΆ
Two commandline applications are provided with msprime
: msp and
mspms. The msp program is an experimental interface for
interacting with the library, and is a POSIX compliant command line
interface. The mspms program is a fullyms compatible
interface. This is useful for those who wish to get started quickly with using
the library, and also as a means of plugging msprime
into existing work
flows. However, there is a substantial overhead involved in translating data
from msprime
‘s native history file into legacy formats, and so new code
should use the Python API where possible.
mspΒΆ
The msp
program provides a convenient interface to the msprime API. It is based on subcommands that either generate or consume a
history file. The simulate
subcommand runs a
simulation storing the results in a file. The other commands are concerned with
converting this file into other formats.
Warning
This tool is very new, and the interface may need to change over time. This should be considered an alpha feature!
msp simulateΒΆ
msp simulate provides a command line interface to the
msprime.simulate()
API function. Using the parameters provided at the
command line, we run a simulation and then save the resulting tree sequence
to the file provided as an argument.
usage: msp simulate [h] [length LENGTH]
[recombinationrate RECOMBINATION_RATE]
[mutationrate MUTATION_RATE]
[effectivepopulationsize EFFECTIVE_POPULATION_SIZE]
[randomseed RANDOM_SEED] [compress]
sample_size history_file
Positional ArgumentsΒΆ
sample_size  The number of individuals in the sample 
history_file  The msprime history file in HDF5 format 
Named ArgumentsΒΆ
–length, L  The length of the simulated region in base pairs. 
–recombinationrate, r  
The recombination rate per base per generation  
–mutationrate, u  
The mutation rate per base per generation  
–effectivepopulationsize, N  
The effective population size Ne  
–randomseed, s  
The random seed. If not specified one is chosen randomly  
–compress, z  Enable HDF5’s transparent zlib compression 
Note
The way in which recombination and mutation rates are specified is different to ms. In ms these rates are scaled by the length of the simulated region, whereas we use rates per unit distance. The rationale for this change is simplify running simulations on a variety of sequence lengths, so that we need to change only parameter and not three simultaneously.
msp upgradeΒΆ
msp upgrade is a command line tool to convert tree sequence files written by older versions of msprime to the latest version. This tool requires h5py, so please ensure that it is installed. The upgrade process involves creating a new tree sequence file from the records stored in the older file and is nondestructive.
usage: msp upgrade [h] [removeduplicatepositions] source destination
Positional ArgumentsΒΆ
source  The source msprime history file in legacy HDF5 format 
destination  The filename of the upgraded copy. 
Named ArgumentsΒΆ
–removeduplicatepositions, d  
Remove any duplicated mutation positions in the source file. 
msp vcfΒΆ
msp vcf is a command line interface to the
msprime.TreeSequence.write_vcf()
method. It prints out the coalescence
vcf in a history file in a tabdelimited text format.
usage: msp vcf [h] [ploidy PLOIDY] history_file
Positional ArgumentsΒΆ
history_file  The msprime history file in HDF5 format 
Named ArgumentsΒΆ
–ploidy, P  The ploidy level of samples 
msp newickΒΆ
msp newick prints out the marginal genealogies in the tree sequence in newick format.
usage: msp newick [h] [precision PRECISION] history_file
Positional ArgumentsΒΆ
history_file  The msprime history file in HDF5 format 
Named ArgumentsΒΆ
–precision, p  
The number of decimal places in branch lengths 
Todo
Document the nodes, edgesets, sites and mutations commands.
mspmsΒΆ
The mspms program is an mscompatible
command line interface to the msprime
library. This interface should
be useful for legacy applications, where it can be used as a dropin
replacement for ms. This interface is not recommended for new applications,
particularly if the simulated trees are required as part of the output
as Newick is very inefficient. The Python API is the recommended interface,
providing direct access to the structures used within msprime
.
Supported FeaturesΒΆ
mspms supports a subset of ms‘s functionality. Please open an issue on GitHub if there is a feature of ms that you would like to see added. We currently support:
 Basic functionality (sample size, replicates, tree and haplotype output);
 Recombination (via the
r
option);  Spatial structure with arbitrary migration matrices;
 Support for ms demographic events. (The implementation of the
es
option is limited, and has restrictions on how it may be combined with other options.)
Geneconversion is not currently supported, but is planned for a future release.
Argument detailsΒΆ
This section provides the detailed listing of the arguments to
mspms (also available via mspms help
). See
the documentation for ms
for details on how these values should be interpreted.
mspms is an mscompatible interface to the msprime library. It simulates the coalescent with recombination for a variety of demographic models and outputs the results in a textbased format. It supports a subset of the functionality available in ms and aims for full compatibility.
usage: mspms [h] [mutationrate theta] [trees]
[recombination rho num_loci] [structure value [value ...]]
[migrationmatrixentry dest source rate]
[migrationmatrix entry [entry ...]]
[migrationratechange t x]
[migrationmatrixentrychange time dest source rate]
[migrationmatrixchange entry [entry ...]]
[growthrate alpha]
[populationgrowthrate population_id alpha]
[populationsize population_id size]
[growthratechange t alpha]
[populationgrowthratechange t population_id alpha]
[sizechange t x] [populationsizechange t population_id x]
[populationsplit t dest source]
[admixture t population_id proportion]
[randomseeds x1 x2 x3] [precision PRECISION] [V]
[f FILENAME]
sample_size num_replicates
Positional ArgumentsΒΆ
sample_size  The number of individuals in the sample 
num_replicates  Number of independent replicates 
Named ArgumentsΒΆ
V, –version  show program’s version number and exit 
f, –filename  Insert commands from a file at this point in the command line. 
BehaviourΒΆ
–mutationrate, t  
Mutation rate theta=4*N0*mu  
–trees, T  Print out trees in Newick format 
–recombination, r  
Recombination at rate rho=4*N0*r where r is the rate of recombination between the ends of the region being simulated; num_loci is the number of sites between which recombination can occur 
Structure and migrationΒΆ
–structure, I  
Sample from populations with the specified deme structure. The arguments are of the form ‘num_populations n1 n2 ... [4N0m]’, specifying the number of populations, the sample configuration, and optionally, the migration rate for a symmetric island model  
–migrationmatrixentry, m  
Sets an entry M[dest, source] in the migration matrix to the specified rate. source and dest are (1indexed) population IDs. Multiple options can be specified.  
–migrationmatrix, ma  
Sets the migration matrix to the specified value. The entries are in the order M[1,1], M[1, 2], ..., M[2, 1],M[2, 2], ..., M[N, N], where N is the number of populations.  
–migrationratechange, eM  
Set the symmetric island model migration rate to x / (npop  1) at time t  
–migrationmatrixentrychange, em  
Sets an entry M[dest, source] in the migration matrix to the specified rate at the specified time. source and dest are (1indexed) population IDs.  
–migrationmatrixchange, ema  
Sets the migration matrix to the specified value at time t.The entries are in the order M[1,1], M[1, 2], ..., M[2, 1],M[2, 2], ..., M[N, N], where N is the number of populations. 
DemographyΒΆ
–growthrate, G  
Set the growth rate to alpha for all populations.  
–populationgrowthrate, g  
Set the growth rate to alpha for a specific population.  
–populationsize, n  
Set the size of a specific population to size*N0.  
–growthratechange, eG  
Set the growth rate for all populations to alpha at time t  
–populationgrowthratechange, eg  
Set the growth rate for a specific population to alpha at time t  
–sizechange, eN  
Set the population size for all populations to x * N0 at time t  
–populationsizechange, en  
Set the population size for a specific population to x * N0 at time t  
–populationsplit, ej  
Move all lineages in population dest to source at time t. Forwards in time, this corresponds to a population split in which lineages in source split into dest. All migration rates for population source are set to zero.  
–admixture, es  
Split the specified population into a new population, such that the specified proportion of lineages remains in the population population_id. Forwards in time this corresponds to an admixture event. The new population has ID num_populations + 1. Migration rates to and from the new population are set to 0, and growth rate is 0 and the population size for the new population is N0. 
MiscellaneousΒΆ
–randomseeds, seeds  
Random seeds (must be three integers)  
–precision, p  
Number of values after decimal place to print 
If you use msprime in your work, please cite the following paper: Jerome Kelleher, Alison M Etheridge and Gilean McVean (2016), “Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes”, PLoS Comput Biol 12(5): e1004842. doi: 10.1371/journal.pcbi.1004842
Tree Sequence FormatsΒΆ
The correlated genealogical trees that describe the shared ancestry of set of
samples are stored concisely in msprime
as a sequence of coalescent events,
represented by a collection of easytounderstand tables. These are output by
coalescent simulation in msprime
or can be read in from another source. To
make this information as efficient and easy as possible to use, we store the
data on disk in a HDF5 based file format.
This page fully documents these tables and the associated HDF5 format, allowing
efficient and convenient access to the genealogical data generated by
msprime
outside of the native Python API. Using the
specification defined here, it should be straightforward to access tree
sequence information in any language with HDF5 support, or to
store genealogies from other sources efficiently using msprime
.
DefinitionsΒΆ
To begin, here are definitions of some key ideas encountered later. This will
define the terminology, as well as giving properties of the tables that these
are stored in. These are properties that can be assumed when writing methods
that operate on an msprime
tree sequence; the function sort_tables
is
provided to put unsorted tables in the proper order.
First are those that describe genealogical relationships:
 tree
 A “gene tree”, i.e., the genealogical tree describing how each of the individuals at the tips of the tree are related to each other. A “tree sequence” contains information sufficient to reconstruct the genealogical tree relating all samples to each other at any point along the genome.
 node
Each branching point in each tree is associated with a particular ancestor, called “nodes”. Since each node represents a certain ancestor, it has a unique
time
, thought of as her birth time, which determines the height of any branching points she is associated with. A given node will be associated with branching points of all trees across a region if that node is the most recent common ancestor to the subtending tips across that region. For each node, we record:(flags, population, time)
where
flags
records information about the ancestor;population
is the integer ID of the ancestor’s (birth) population, andtime
is how long ago the ancestor was born. samples
 The tips of the tree, that we have obtained data from. These are
distinguished from other nodes by the fact that a tree sequence must
describe the genealogical history of all samples at every point on the
genome. These are a special kind of node, having
flags
set to 1 (as a binary mask).  edgeset
Tree sequences are constructed by specifying over which segments of genome which nodes inherit from which other nodes. This information is stored by recording:
(left, right, parent, children)
where each node in the list
children
inherits from the nodeparent
on the halfopen interval of chromosome[left, right)
.
Here are the formal requirements for a set of nodes and edgesets to make sense,
and to allow msprime
‘s algorithms to work properly.
To disallow time travel and multiple inheritance:
 Offspring must be born after their parents (and hence, no loops).
 The set of intervals on which each individual is a child must be disjoint.
and for algorithmic reasons:
 The leftmost endpoint of each chromosome is 0.0.
 Node times must be strictly greater than zero.
 The set of intervals on which each individual is a parent must be disjoint.
 The list of offspring in an edgeset must be sorted.
 Edgesets must be sorted in nondecreasing time order.
 Each edgeset must contain at least two children.
Note that since each node time is equal to the (birth) time of the corresponding parent, time is measured in clock time (not meioses).
MutationsΒΆ
In addition to genealogical relationships, msprime
generates and stores
mutations. Associating these with nodes means that a variant shared by many
individuals need only be stored once, allowing retrieval and processing of
variant information much more efficiently than if every individual’s genotype
was stored directly.
 mutation
This type records a mutation that has occurred at some point in the genealogical history. Each mutation is associated with a particular
node
(i.e., a particular ancestor), so that any sample which inherits from that node will also inherit that mutation, unless another mutation intervenes. The type records:site node derived_state 0 14 1
Here
site
is the index of thesite
at which the mutation occurred,node
records the ID of the ancestral node associated with the mutation, andderived_state
is the allele that any sample inheriting from that node at this site will have if another mutation does not intervene. Thenode
is not necessarily the ancestor in whom the mutation occurred, but rather the ancestor at the bottom of the branch in the tree at that site on which the mutation occurred. site
Rather than storing a position on the genome directly, a
mutation
stores the index of asite
, that describes that position. This is to allow efficient processing of multiple mutations at the same genomic position. Asite
records a position on the genome where a mutation has occurred along with the ancestral state (i.e., the state at the root of the tree at that position):id position ancestral_state 0 0.1 0
The
id
is not stored directly, but is implied by its index in the site table.
To allow for efficent algorithms, it is required that
 Sites are sorted by increasing position,
 and mutations are sorted by site.
Mutations at the same site may currently be in any order, but this may change.
MigrationsΒΆ
In simulations trees can be thought of as spread across space, and it is helpful for inferring demographic history to record this history. This is stored using the following type.
 migration
Migrations are performed by individual ancestors, but most likely not by an individual tracked as a
node
(as in a discretedeme model they are unlikely to be both a migrant and a most recent common ancestor). So,msprime
records when a segment of ancestry has moved between populations:left right node source dest time 0.0 0.3 3 0 1 2.1
This
migration
records that the ancestor who was alive 2.1 time units in the past from whichnode
3 inherited the segment of genome between 0.0 and 0.3 migrated from population 0 to population 1.
A valid migration
:
 Has
time
strictly between the time of itsnode
and the time of any ancestral node from which that node inherits on the segment[left, right)
.  Has the
population
of any such ancestor matchingsource
, if anothermigration
does not intervene.
Working with TablesΒΆ
Here is an example. Consider the following sequence of trees:
time

1.0 6
0.7 / \ 5
/ x / \
0.5 / 4 4 / 4
/ / \ / x / / \
0.4 / / \ / 3 / / \
/ / \ / / \ / / \
/ / \ / / x / / \
/ / \ / / \ / / \
0.0 0 1 2 1 0 2 0 1 2
position 0.0 0.2 0.8 1.0
First, we specify the nodes in a NodeTable
:
id is_sample population time
0 1 0 0
1 1 0 0
2 1 0 0
3 0 0 0.4
4 0 0 0.5
5 0 0 0.7
6 0 0 1.0
Recall that the first column, id
, is not actually recorded, only provided
for convenience. This has three samples: nodes 0, 1, and 2, and lists their
birth times. Then, we specify the edgesets:
left right parent children
0.2 0.8 3 0,2
0.0 0.2 4 1,2
0.2 0.8 4 1,3
0.8 1.0 4 1,2
0.8 1.0 5 0,4
0.0 0.2 6 0,4
Since node 3 is most recent, the edgeset that says that nodes 0 and 2 inherit from node 3 on the interval between 0.2 and 0.8 comes first. Next are the edgesets from node 4: there are three of these, for each of the three genomic intervals over which node 4 is ancestor to a distinct set of nodes. At this point, we know the full tree on the middle interval. Finally, edgesets specifying the common ancestor of 0 and 4 on the remaining intervals (parents 6 and 5 respectively) allow us to construct all trees across the entire interval.
In the depiction above, x
denotes mutations. Suppose that the first
mutation occurs at position 0.1 and the mutations in the second tree both
occurred at the same position, at 0.5 (with a back mutation). The positions
are recorded in the sites table:
id position ancestral_state
0 0.1 0
1 0.5 0
and the acutal mutations:
site node derived_state
0 4 1
1 3 1
1 2 0
This would then result in the following (twolocus) haplotypes for the three samples:
sample haplotype
 
0 01
1 10
2 11
Tables APIΒΆ
Tables provide a convenient method for viewing, importing and exporting tree
sequences. msprime
provides direct access to the the columns of a table as
numpy
arrays: for instance, if n
is a NodeTable
, then n.time
will return an array containing the birth times of the individuals in the
table. However, it is important to note that this is not a shallow copy:
modifying n.time
will not change the node table n
. This may change in
the future, but currently there are two ways to modify tables: .add_row()
and .set_columns()
(and also .reset()
, which empties the table).
The example node table above would be constructed using .add_row()
as
follows:
n = msprime.NodeTable()
sv = [True, True, True, False, False, False, False]
tv = [0.0, 0.0, 0.0, 0.4, 0.5, 0.7, 1.0]
pv = [0, 0, 0, 0, 0, 0, 0]
for s, t, p in zip(sv, tv, pv):
n.add_row(flags=s, population=p, time=t)
print(n)
The .add_row()
method is natural (and should be reasonably efficient) if
new records appear onebyone. In the example above it would have been more
natural to use .set_columns()
:
n = msprime.NodeTable()
n.set_columns(flags=sv, population=pv, time=tv)
Finally, here is an example where we add 1.4 to every time
except the first
in the NodeTable constructed above using numpy
indexing:
fn = n.flags
pn = n.population
tn = n.time
tn[1:] = tn[1:] + 1.4
n.set_columns(flags=fn, population=pn, time=tn)
Sorting tablesΒΆ
..autofunction:: msprime.sort_tables
Import and exportΒΆ
This section describes how to extract tables from a TreeSequence
, and how
to construct a TreeSequence
from tables. Since tree sequences are
immutible, often the best way to modify a TreeSequence
is something along
the lines of (for ts
a TreeSequence
):
nodes = msprime.NodeTable()
edgesets = msprime.EdgesetTable()
ts.dump_tables(nodes=nodes, edgesets=edgesets)
# (modify nodes and edgesets)
ts.load_tables(nodes=nodes, edgesets=edgesets)

classmethod
TreeSequence.
load_tables
(**kwargs)ΒΆ

TreeSequence.
dump_tables
(nodes=None, edgesets=None, migrations=None, sites=None, mutations=None) Copy the contents of the tables underlying the tree sequence to the specified objects.
Parameters:  nodes (NodeTable) – The NodeTable to load the nodes into.
 edgesets (EdgesetTable) – The EdgesetTable to load the edgesets into.
 migrations (MigrationTable) – The MigrationTable to load the migrations into.
 sites (SiteTable) – The SiteTable to load the sites into.
 mutations (MutationTable) – The NodeTable to load the mutations into.
Returns: A TableTuple containing all tables underlying the tree sequence.
Return type: TableTuple
HDF5 FormatΒΆ
The file format is broken into a number of groups. Each group contains datasets to define the data along with attributes to provide necessary contextual information.
The root group contains one attributes, format_version
. This
is a pair (major, minor)
describing the file format version. This
document describes version 3.2.
Path  Type  Dim  Description 

/format_version  H5T_STD_U32LE  2  The (major, minor) file format version. 
Provenance datasetΒΆ
The provenance dataset records information relating the the provenance of a particular tree sequence file. When a tree sequence file is generated all the information required to reproduce the file should be encoded as a string and stored in this dataset. Subsequent modifications to the file should be also be recorded and appended to the list of strings.
The format of these strings is implementation defined. In the
current version of msprime
provenance information is encoded
as JSON. This information is incomplete, and will be updated in future
versions.
Path  Type  Dim  Description 

/provenance  H5T_STRING  Scalar  Provenance information. 
Mutations groupΒΆ
The mutations
group is optional, and describes the location of mutations
with respect to tree nodes and their positions along the sequence. Each mutation
consists of a node (which must be defined in the trees
group) and a
position. Positions are defined as a floating point value to allow us to
express infinite sites mutations. A mutation position \(x\) is defined on the same
scale as the genomic coordinates for trees, and so we must have
\(0 \leq x < L\), where \(L\) is the largest value in the
/trees/breakpoints
dataset.
As for the coalescence records in the trees
group, mutation records are
stored as seperate vectors for efficiency reasons. Mutations must be stored
in nondecreasing order of position.
Path  Type  Dim 

/mutations/node  H5T_STD_U32LE  M 
/mutations/position  H5T_IEEE_F64LE  M 
Trees groupΒΆ
The trees
group is mandatory and describes the topology of the tree
sequence. The trees
group contains a number of nested groups and datasets,
which we will describe in turn.
Breakpoints datasetΒΆ
The /trees/breakpoints
dataset records the floating point positions of the
breakpoints between trees in the tree sequence, and the flanking positions
\(0\) and \(L\). Positions in the /trees/records
group refer to
(zero based) indexes into this array. The first breakpoint must be zero, and
they must be listed in increasing order.
Path  Type 

/trees/breakpoints  H5T_IEEE_F64LE 
Nodes groupΒΆ
The /trees/nodes
group records information about the individual
nodes in a tree sequence. Leaf nodes (from \(0\) to \(n  1\))
represent the samples and internal nodes (\(\geq n\)) represent
their ancestors. Each node corresponds to a particular individual that
lived at some time time in the history of the sample. The nodes
group is used to record information about these individuals.
Path  Type 

/trees/nodes/population  H5T_STD_U32LE 
/trees/nodes/time  H5T_IEEE_F64LE 
Records groupΒΆ
The /trees/records
group stores the individual coalesence records.
Each record consists of four pieces of information: the left and
right coordinates of the coalescing interval, the list of child nodes
and the parent node.
The left
and right
datasets are indexes into the /trees/breakpoints
dataset and define the genomic interval over which the record applies. The
interval is halfopen, so that the left coordinate is inclusive and the right
coordinate is exclusive.
The node
dataset records the parent node of the record, and is
an index into the /trees/nodes
group.
The num_children
dataset records the number of children for a particular
record. The children
dataset then records the actual child nodes for each
coalescence record. This 1dimensional array lists the child nodes for every
record in order, and therefore by using the num_children
array we can
efficiently recover the actual children involved in each event. Within a given
event, child nodes must be sorted in increasing order. The records must be
listed in time increasing order.
Path  Type  Dim 

/trees/left  H5T_STD_U32LE  N 
/trees/right  H5T_STD_U32LE  N 
/trees/node  H5T_STD_U32LE  N 
/trees/num_children  H5T_STD_U32LE  N 
/trees/children  H5T_STD_U32LE  \(\leq 2 \times\) N 
Indexes groupΒΆ
The /trees/indexes
group records information required to efficiently
reconstruct the individual trees from the tree sequence. The
insertion_order
dataset contains the order in which records must be applied
and the removal_order
dataset the order in which records must be
removed for a lefttoright traversal of the trees.
Path  Type 

/trees/indexes/insertion_order  H5T_STD_U32LE 
/trees/indexes/removal_order  H5T_STD_U32LE 
Developer documentationΒΆ
If you would like to add some features to msprime
, please read the
following. If you think there is anything missing,
please open an issue or
pull request on GitHub!
QuickstartΒΆ
 Make a fork of the msprime repo on GitHub
 Clone your fork into a local directory.
 Install the basic requirements.
 Install the Python development requirements using
pip install r requirements/development.txt
.  Build the low level module by running
make
in the project root. If you are using Python 2.7, runmake ext2
and if you are using Python 3.x, runmake ext3
.  Run the tests to ensure everything has worked:
nosetests vs
. These should all pass.  Make your changes in a local branch, and open a pull request on GitHub when you are ready. Please make sure that (a) the tests pass before you open the PR; and (b) your code passes PEP8 checks (see below for a git commit hook to ensure this happens automatically) before opening the PR.
Continuous integration testsΒΆ
Three different continuous integration providers are used, which run different combinations of tests on different platforms:
 Travis CI runs tests on Linux and OSX using the Conda infrastructure for the system level requirements. All supported versions of Python are tested here.
 CircleCI Runs all Python tests using the aptget infrastructure for system requirements. Additionally, the lowlevel tests are run, coverage statistics calculated using CodeCov, and the documentation built.
 AppVeyor Runs Python tests on 32 and 64 bit Windows using conda.
OverviewΒΆ
There are three main parts of msprime
, in increasing order of complexity:
 Highlevel Python. The PythonAPI and command line interface tools are all defined
in the
msprime
directory.  C library. The underlying highperformance C code is written as a standalone library.
All of the code for this library is in the
lib
directory.  Lowlevel PythonC interface. The interface between the Python and C code is the
_msprimemodule.c
file, which defines the_msprime
module.
Each of these aspects has its own coding conventions and development tools, which are documented in the following sections.
Highlevel PythonΒΆ
Throughout this document, we assume that the msprime
package is built and
run locally _within_ the project directory. That is, msprime
is _not_ installed
into the Python installation using pip install e
or setuptools development
mode. Please
ensure that you build the lowlevel module using (e.g.) make ext3
and that
the shared object file is in the project root.
ConventionsΒΆ
All Python code follows the PEP8 style guide, and is checked using the flake8 tool as part of the continuous integration tests. In particular, lines must be no longer than 89 characters.
To avoid failing CI tests, it’s a good idea to install a local commit hook to automatically check
that code conforms to PEP8 before committing. Adding this to your .git/hooks/precommit
should do the trick:
# Run flake8 to check for lint errors.
exec flake8 maxlinelength 89 setup.py msprime tests
PackagingΒΆ
msprime
is packaged and distributed as Python module, and follows the current
bestpractices advocated by the
Python Packaging Authority. The primary means of
distribution is though PyPI, which provides the
canonical source for each release.
TestsΒΆ
The tests for the highlevel code are in the tests
directory, and run using
nose. A lot of the simulation and basic
tests are contained in the tests/test_highlevel.py
file, but more recently
smaller test files with more focussed tests are preferred (e.g., test_vcf.py
,
test_demography.py
).
All new code must have high test coverage, which will be checked as part of the continuous integration tests by CodeCov.
Interfacing with lowlevel moduleΒΆ
Much of the highlevel Python code only exists to provide a simpler interface to
the lowlevel _msprime
module. As such, many objects (such as TreeSequence
)
are really just a shallow layer on top of the corresponding lowlevel object.
The convention here is to keep a reference to the lowlevel object via
a private instance variable such as self._ll_tree_sequence
.
Command line interfacesΒΆ
The command line interfaces for msprime
are defined in the msprime/cli.py
file.
Each CLI has a single entry point (e.g. msp_main
) which is invoked to run the
program. These entry points are registered with setuptools
using the
console_scripts
argument in setup.py
, which allows them to be deployed as
firstclass executable programs in a crossplatform manner.
There are simple scripts in the root of the project (currently: msp_dev.py
,
mspms_dev.py
) which are used for development. For example, to run the
development version of mspms
use python mspms_dev.py
.
C LibraryΒΆ
The lowlevel code for msprime
is written in C, and is structured as a
standalone library. This code is all contained in the lib
directory.
Although the code is structured as a library, it is not intended to be used
outside of the msprime
project! The interfaces at the C level change
considerably over time, and are deliberately undocumented.
BasicsΒΆ
To compile and develop the C code, a few extra development libraries are needed. Libconfig is used for the development CLI and CUnit for unit tests. On Debian/Ubuntu, these can be installed using
$ sudo aptget install libcunit1dev libconfigdev
Compile the code locally run make
in the lib
directory.
Development CLIΒΆ
When developing the C code, it is usually best to use the development CLI to invoke the code. This is much simpler than going through the Python interface, and allows tools such as valgrind to be used directly. For example, when developing new simulation functionality, you should get the basic work done using the CLI and only move over to the Python API once you are reasonably sure that the code works properly.
The development CLI is written using libconfig to parse the simulation parameters
file, and argtable3 to parse the
command line arguments. The argtable3
code is included in the source (but
not used in the distributed binaries, since this is strictly a development
tool).
The CLI is run as follows:
$ ./main <command> <arguments>
Running the main
program without arguments will print out a summary of the
options.
The most important command for simulator development is simulate
,
which takes a configuration file as a parameter and writes the resulting
simulation to an output file in HDF5 format. For example,
$ ./main simulate dev.cfg o out.hdf5
The development configuration file describes the simulation that we want to
run, and uses the
libconfig syntax.
An example is given in the file dev.cfg
which should have sufficient documentation
to be selfexplanatory.
Unit TestsΒΆ
The Clibrary has an extensive suite of unit tests written using
CUnit. These tests aim to establish that the
lowlevel APIs work correctly over a variety of inputs, and particularly, that
the tests don’t result in leaked memory or illegal memory accesses. The tests should be
periodically run under valgrind to make sure of this. To run all the tests, type
./tests
. To run a specific test, provide this test name as a command line argument,
e.g.:
$ ./tests fenwick_tree
While 100% test coverage is not feasible for C code, we aim to cover all code that can be reached. (Some classes of error such as malloc failures and IO errors are difficult to simulate in C.) Code coverage statistics are automatically tracked using CodeCov.
Coding conventionsΒΆ
The code is written using the C99 standard. All variable declarations should be done at the start of a function, and functions kept short and simple where at all possible.
No global or module level variables are used for production code.
The code is organised following objectoriented principles. Each ‘class’ is defined using
a struct, which encapsulates all the data it requires. Every ‘method’ on this class
is then a function that takes this struct as its first parameter. Each class has
an alloc
method, which is responsible for allocating memory and a free
method
which frees all memory used by the object. For example, the
Fenwick tree class is defined as
follows:
typedef struct {
size_t size;
size_t log_size;
int64_t *tree;
int64_t *values;
} fenwick_t;
int fenwick_alloc(fenwick_t *self, size_t initial_size);
int fenwick_free(fenwick_t *self);
int64_t fenwick_get_total(fenwick_t *self);
This defines the fenwick_t
struct, and alloc and free methods and a method
to return the total of the tree. Note that we follow the Python convention
and use self
to refer to the current instance.
Most objects also provide a print_state
method, which is useful for
debugging.
This objectoriented structure means that the vast majority of the code is
fully thread safe. The only exceptions to this rule is the msp_strerror
,
tree_sequence_load
and tree_sequence_dump
functions which are not
threadsafe due to their interaction with HDF5’s error handling code.
Error handlingΒΆ
A critical element of producing reliable C programs is consistent error handling and checking of return values. All return values must be checked! In msprime, all functions (except the most trivial accessors) return an integer to indicate success or failure. Any negative value is an error, and must be handled accordingly. The following pattern is canonical:
ret = msp_do_something(self, argument);
if (ret != 0) {
goto out;
}
// rest of function
out:
return ret;
Here we test the return value of msp_do_something
and if it is nonzero,
abort the function and return this same value from the current function. This
is a bit like throwing an exception in higherlevel languages, but discipline
is required to ensure that the error codes are propagated back to the original
caller correctly.
Particular care must be taken in functions that allocate memory, because we must ensure that this memory is freed in all possible success and failure scenarios. The following pattern is used throughout for this purpose:
double x = NULL;
x = malloc(n * sizeof(double));
if (x == NULL) {
ret = MSP_ERR_NO_MEMORY;
goto out;
}
// rest of function
out:
if (x != NULL) {
free(x);
}
return ret;
It is vital here that x
is initialised to NULL
so that we are guaranteed
correct behaviour in all cases. For this reason, the convention is to declare all
pointer variables on a single line and to initialise them to NULL
as part
of the declaration.
Error codes are defined in err.h
, and these can be translated into a
message using msp_strerror(err)
.
Running valgrindΒΆ
Valgrind is an essential development tool, and is used extensively. (Being able to run valgrind was one of the motivating factors in the Clibrary architecture. It is difficult to run valgrind on a Python extension module, and so the simplest way to ensure that the lowlevel code is memorytight is to separate it out into an independent library.)
Unfortunately due to a bug in HDF5, when running valgrind on either the tests or the development CLI, it appears that there is a memory leak:
$ valgrind ./tests fenwick_tree
==23308== Memcheck, a memory error detector
==23308== Copyright (C) 20022015, and GNU GPL'd, by Julian Seward et al.
==23308== Using Valgrind3.11.0 and LibVEX; rerun with h for copyright info
==23308== Command: ./tests fenwick_tree
==23308==
CUnit  A unit testing framework for C  Version 2.13
http://cunit.sourceforge.net/
Suite: msprime
Test: fenwick_tree ...passed
Run Summary: Type Total Ran Passed Failed Inactive
suites 1 0 n/a 0 0
tests 74 1 1 0 0
asserts 39798 39798 39798 0 n/a
Elapsed time = 0.342 seconds
==23308==
==23308== HEAP SUMMARY:
==23308== in use at exit: 1,360 bytes in 3 blocks
==23308== total heap usage: 12,752 allocs, 12,749 frees, 8,295,436 bytes allocated
==23308==
==23308== LEAK SUMMARY:
==23308== definitely lost: 0 bytes in 0 blocks
==23308== indirectly lost: 0 bytes in 0 blocks
==23308== possibly lost: 0 bytes in 0 blocks
==23308== still reachable: 1,360 bytes in 3 blocks
==23308== suppressed: 0 bytes in 0 blocks
==23308== Rerun with leakcheck=full to see details of leaked memory
==23308==
==23308== For counts of detected and suppressed errors, rerun with: v
==23308== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
Note the “1,360 bytes in 3 blocks” reported as lost. This is harmless, and can be ignored.
Python C InterfaceΒΆ
OverviewΒΆ
The Python C interface is written using the
Python C API and the code is in the
_msprimemodule.c
file. When compiled, this produces the _msprime
module,
which is imported by the highlevel module. The lowlevel Python module is
not intended to be used directly and may change arbitrarily over time.
The usual pattern in the lowlevel Python API is to define a Python class
which corresponds to a given “class” in the C API. For example, we define
a TreeSequence
class, which is essentially a thin wrapper around the
tree_sequence_t
type from the C library.
The _msprimemodule.c
file follows the standard conventions given in the
Python documentation.
CompilingΒΆ
The setup.py
file descibes the requirements for the lowlevel _msprime
module and how it is built from source. To build the module so that it is available
for use in the current working directory, run
$ python setup.py build_ext inplace
A development Makefile is also provided in the project root, so that running
make ext2
or make ext3
should build the extension module for either
Python 2 or Python 3.
Testing for memory leaksΒΆ
The Python C API can be subtle, and it is easy to get the reference counting wrong.
The stress_lowlevel.py
script makes it easier to track down memory leaks
when they do occur. The script runs the unit tests in a loop, and outputs
memory usage statistics.
Statistical testsΒΆ
To ensure that msprime
is simulating the correct process we run many statistical
tests. Since these tests are quite expensive (taking some hours to run) and
difficult to automatically validate, they are not run as part of CI but instead
as a prerelease sanity check. They are also very useful to run when developing
new simulation functionality, as subtle statistical bugs can easily slip in
unnoticed.
The statistical tests are all run via the verification.py
script in the project root.
The script has some extra dependencies listed in the requirements/verification.txt
,
which can be installed using pip install r
or conda install file
. Run
this script using:
$ python verification.py
Warning
The verification.py
currently does not support Python 3 because of odd
behaviour from dendropy.
The statistical tests depend on compiled programs in the data
directory.
This includes a customised version of ms
and a locally compiled version of
scrm. These programs must be compiled before
running the statistical tests, and can be built by running make
in the
data
directory. If this is successful, there should be several binaries
like ms
and ms_summary_stats
present in the data
directory.
The verification.py
script contains lots of different tests, each one
identified by a particular “key”. To run all the tests, run the script without
any arguments. To run some specific tests, provide the required keys as command
line arguments.
Many of the tests involve creating an ms
command line, running it
line on ms
and msprime
and comparing the statistical properties of the
results. The output of each test is a series of plots, written to a directory
named after test. For example, results for the admixture1pop2
test are
written in the tmp__NOBACKUP__/admixture1pop2/
directory (the prefix is
not important here and can be changed). The majority of the results are
QQplots of the statistics in question comparing ms
and msprime
.
There are also several “analytical” tests, which compare the distributions of
values from msprime
with analytical expectations.
DocumentationΒΆ
Documentation is written using Sphinx
and contained in the docs
directory. It is written in the
reStructuredText format and
is deployed automatically to readthedocs. To
build the documentation locally run make
in the docs
directory.
This should build the HTML documentation in docs/_build/html/
.
Citing msprimeΒΆ
If you use msprime
in your work, please cite the
PLoS Computational Biology paper:
Jerome Kelleher, Alison M Etheridge and Gilean McVean (2016), Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes, PLoS Comput Biol 12(5): e1004842. doi: 10.1371/journal.pcbi.1004842
Bibtex record:
@article{10.1371/journal.pcbi.1004842,
author = {Kelleher, Jerome AND Etheridge, Alison M AND McVean, Gilean},
journal = {PLoS Comput Biol},
title = {Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes},
year = {2016},
month = {05},
volume = {12},
url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1004842},
pages = {122},
number = {5},
doi = {10.1371/journal.pcbi.1004842}
}