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Merge pull request #182 from hyanwong/doc-URL-corrections
Replace most refs to msprime to refer to tskit
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docs/file_formats.rst

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@@ -42,12 +42,12 @@ Tree sequences
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The goal of ``tsinfer`` is to infer correlated genealogies from variation
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data, and it uses the very efficient `succinct tree sequence
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<http://msprime.readthedocs.io/en/stable/interchange.html>`_ data structure
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to encode this output. Please see the `msprime documentation
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<http://msprime.readthedocs.io/en/stable>`_ for details on how to
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<https://tskit.readthedocs.io/en/latest/data-model.html>`_ data structure
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to encode this output. Please see the `tskit documentation
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<https://tskit.readthedocs.io/>`_ for details on how to
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process and manipulate such tree sequences.
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The intermediate ``.ancestors.trees`` file produced by the
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:ref:`sec_inference_match_ancestors` step is also a
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tree sequence and can be loaded and analysed using the
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`msprime API <http://msprime.readthedocs.io/en/stable/api.html>`_.
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`tskit API <https://tskit.readthedocs.io/en/latest/python-api.html>`_.

docs/inference.rst

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@@ -36,13 +36,12 @@ Data model
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**********
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The data model for ``tsinfer`` is tightly integrated with
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``msprime``'s `data model <https://msprime.readthedocs.io/en/stable/interchange.html>`_
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``tskit``'s `data model <https://tskit.readthedocs.io/en/latest/data-model.html>`_
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and uses the same concepts throughout. The intermediate file formats and APIs
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described here provide a bridge between this model and existing data sources. For
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convenience, we provide a brief description of concepts needed for importing
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data into ``tsinfer`` here. Please see the `msprime documentation
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<https://msprime.readthedocs.io/en/stable/index.html>`_ for more detailed
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information.
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data into ``tsinfer`` here. Please see the `tskit documentation
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<https://tskit.readthedocs.io/>`_ for more detailed information.
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.. _sec_inference_data_model_individual:
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@@ -167,8 +166,9 @@ number of recombination events.
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The copying path for each ancestor then describes its ancestry at every
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point in the sequence: from a genealogical perspective, we know its
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parent node. This information is encoded precisely as an `edge
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<http://msprime.readthedocs.io/en/stable/interchange.html#edge-table>`_ in a
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`tree sequence <http://msprime.readthedocs.io/en/stable/interchange.html#data-model>`_.
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<https://tskit.readthedocs.io/en/latest/data-model.html#edge-table>`_ in a
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`tree sequence
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<https://tskit.readthedocs.io/en/latest/data-model.html>`_.
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Thus, we refer to the output of this step as the "ancestors tree sequence",
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which is conventionally stored in a file ending with ``.ancestors.trees``.
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3. Reduce the resulting tree sequence to a canonical form by
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`simplifying it
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<http://msprime.readthedocs.io/en/stable/api.html#msprime.TreeSequence.simplify>`_.
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<https://tskit.readthedocs.io/en/latest/python-api.html#tskit.TreeSequence.simplify>`_.
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.. todo::
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1. Describe path compression here and above in the ancestors

docs/installation.rst

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@@ -14,10 +14,6 @@ e.g.::
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will install ``tsinfer`` to the Python installation corresponding to your
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``python3`` executable. All requirements should be installed automatically.
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However, there are situations (usually where the GSL libraries are not in the default
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locations) where ``msprime`` installation can fail. Please the
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`msprime installation documentation <https://msprime.readthedocs.io/en/stable/installation.html>`_
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for details on the various to address this problem.
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To run the command line interface to ``tsinfer`` you can then use::
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$ source tsinfer-venv/bin/activate
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(tsinfer-venv) $ pip install tsinfer
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(tsinfer-venv) $ tsinfer --help
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.. _sec_installation_installation_problems:
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****************
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Potential issues
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****************
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One of the dependencies of ``tsinfer``,
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`numcodecs <https://numcodecs.readthedocs.io/>`_, is compiled to
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use AVX2 instructions (where available) when installed using pip. This can lead to
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issues when ``numcodecs`` is compiled on a machine that supports AVX2
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and subsequently run on older machines that do not. To resolve this, ``numcodecs`` has a
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``DISABLE_NUMCODECS_AVX2`` variable which can be turned on before calling
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``pip install``, see
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`these instructions <https://numcodecs.readthedocs.io/en/stable/#installation>`_
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for details.

docs/introduction.rst

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@@ -17,6 +17,6 @@ make two very important gains:
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storing and processing the data that we have.
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The output of ``tsinfer`` is an :class:`msprime.TreeSequence` and so the
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full `msprime API <https://msprime.readthedocs.io/>`_ can be used to
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analyse real data, in precisely the same way that it is currently used
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to analyse simulation data.
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full `tskit API <https://tskit.readthedocs.io>`_ can be used to
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analyse real data, in precisely the same way that it is commonly used
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to analyse simulation data, for example, from `msprime <https://msprime.readthedocs.io/>`_.

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