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(sec_intro)=
This site contains a number of tutorials to develop your understanding of
genetic genealogies, ancestral recombination graphs, and the
succinct tree sequence storage format,
as implemented in tskit: the tree sequence toolkit.
Also included are a number of tutorials showing advanced use of
software programs,
such as msprime, that form part of the
tskit ecosystem.
:tags: [remove-input]
import math
import msprime
def make_7_tree_4_tip_ts():
ts = msprime.sim_ancestry(
4, ploidy=1, random_seed=889, sequence_length=1000, recombination_rate=0.001)
ts = msprime.sim_mutations(ts, rate=2e-3, random_seed=123)
# Check we have picked a random seed that gives a nice plot of 7 trees
tip_orders = {
tuple(u for u in t.nodes(order="minlex_postorder") if t.is_sample(u))
for t in ts.trees()
}
topologies = {tree.rank() for tree in ts.trees()}
assert tip_orders == {(0, 1, 2, 3)} and len(topologies) > 1 and ts.num_trees == 7
return ts
ts = make_7_tree_4_tip_ts()
# Set some parameters: these can be adjusted to your liking
tree_width = 80
height = 200 # Normal height for tree + x-axis
y_step = 20 # Stagger between trees (i.e. 0 for all trees in a horizontal line)
skew = 0.7 # How skewed the trees are, in radians
width = tree_width * ts.num_trees + 20 + 20 # L & R margins in draw_svg = 20px
angle = math.atan(y_step/tree_width)
ax_mv = y_step, (ts.num_trees - 1) * y_step - 90 + math.tan(skew) * (tree_width * .9)
# CSS transforms used to skew the axis and stagger + skew the trees
style = f".x-axis {{transform: translate({ax_mv[0]}px, {ax_mv[1]}px) skewY(-{angle}rad)}}"
for i in range(ts.num_trees):
# Stagger each tree vertically by y_step, transforming the "plotbox" tree container
style += (
f".tree.t{i} > .plotbox " + "{transform:" +
f"translateY({(ts.num_trees - i - 1) * y_step-85}px) skewY({skew}rad)" + "}"
)
# Define a bigger canvas size so we don't crop the moved trees from the drawing
size = (width, height)
canvas_size = (width + y_step, height + math.tan(skew)*tree_width)
ts.draw_svg(size=size, x_scale="treewise", style=style, canvas_size=canvas_size)
If you are new to the world of tree sequences, we suggest you start with the
first tutorial: {ref}sec_what_is
:::{note} Tutorials are under constant development. Those that are still a work in progress and not yet ready for use are shown in italics in the list of tutorials.
We very much welcome help developing existing tutorials or writing new ones. Please open or contribute to a GitHub issue if you would like to help out. :::
In addition to these tutorials, our Learn page lists selected videos and publications to help you learn about tree sequences.
We aim to be a friendly, welcoming open source community.
Questions and discussion about using {program}tskit, the tree sequence toolkit
should be directed to the
GitHub discussion forum, and there are
similar forums for other software in the tree sequence development community,
such as for msprime and
tsinfer.
(sec_intro_running)=
It is possible to run the tutorial code on your own computer, if you wish.
This will allow you to experiment with the examples provided.
The recommended way to do this is from within a
Jupyter notebook. As well as installing Jupyter, you will also
need to install the various Python libraries, most importantly
tskit, msprime, numpy, and matplotlib. These and other packages are
listed in the requirements.txt
file; a shortcut to installing the necessary software is therefore:
python3 -m pip install -r https://tskit.dev/tutorials/requirements.txt
In addition, to run the {ref}R tutorial<sec_tskit_r> you will need to install the R
reticulate library, and if running it in a Jupyter
notebook, the IRkernel library. This can be done by
running the following command within R:
install.packages(c("reticulate", "IRkernel")); IRkernel::installspec()
(sec_intro_downloading_datafiles)=
Many of the tutorials use pre-existing tree sequences stored in the
data directory.
These can be downloaded individually from that link, or you can
download them all at once by running the script stored in
https://tskit.dev/tutorials/examples/download.py.
If you are running the code in the tutorials from within a Jupyter notebook
then you can simply load this code into a new cell by using the
%load cell magic.
Just run the following in a Jupyter code cell:
%load https://tskit.dev/tutorials/examples/download.py
Running the resulting Python code should download the data files, then print out
finished downloading when all files are downloaded. You should then be able
to successfully run code such as the following:
import tskit
ts = tskit.load("data/basics.trees")
print(f"The file 'data/basics.trees' exists, and contains {ts.num_trees} trees")