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hi all;
Just revising the example creating step by step tutorial (GROMACS style)

not finish yet but at least you can have a look.

PS -> there is the possbility to find some "flying" files here and there; don't worry i will clean everything.

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@andrewtarzia andrewtarzia left a comment

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Looking very good!

SimoneMartino98 and others added 12 commits January 23, 2026 09:45
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
Co-authored-by: Andrew Tarzia <andrew.tarzia@gmail.com>
streamline the analysis of many-body trajectories, offering a consistent and
user-friendly interface across most analysis tasks.

This is achieved through the classe, :class:`.trajectory.Trj`, which corresponds to an
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"class" invece di "classe"

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ops..

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@matteobecchi matteobecchi left a comment

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Very nice! Definitely better organized than before.

Now the ``trj`` variable contains the trajectory, and using the methods of the
:class:`.trajectory.Trj` class we can perform all the dynsight analyses on
this trajectory. For instance, let's say we want to compute the LENS
descriptor (`published paper <https://doi.org/10.1073/pnas.2300565120>`__).
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Since in the nex tutorial TimeSOAP is cited as Caruso et al., here it is better to cite lens as "Crippa et al." and not as "published paper"


The :class:`.trajectory.Insight` objects can directly be used to perform post-processing such as smoothing (see the other `tutorials pages <../tutorials_menu.html>`_).
But they can also be used to perform clustering analysis. In this example, we will show how to use the
``Onion Clustering`` method (`published paper <https://doi.org/10.1073/pnas.2403771121>`__) to cluster the LENS values computed above.
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Also here, cite it as Becchi et al.


In this tutorial, we will show you the effect of noise reduction by using
the spatial denoising algorithm implemented in ``dynsight`` (Related paper: `Donkor et al. <https://doi.org/10.1021/acs.jpclett.4c00383>`__, `Martino et al. <https://doi.org/10.1088/2632-2153/adfa66>`__).
We will also explain a new application of the Onion Clustering algorithm to explore different time resolutions for the classification (:class:`.trajectory.Insight.get_onion_analysis()`).
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maybe is not "a new application of Onion Clustering", but "another application of Onion Clustering"

@SimoneMartino98 SimoneMartino98 marked this pull request as ready for review January 26, 2026 14:16
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@ChiaraLionello ChiaraLionello left a comment

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I think now they are very clear and I like how they are organized. Good job!

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Looks good! Some comments still to do, and we discussed using doctests in the examples still.

topo_file=files_path / "ice_water_ox.gro",
)
In this tutorial, we will use the descriptor ``TimeSOAP`` ( `Caruso et al. <https://doi.org/10.1063/5.0147025>`__).
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remove space before Caruso

time window the one that corresponds to the highest number of clusters and longest ``delta_t`` (in this case, ``delta_t=37`` frames).
This choice should guarantee that the clusters identified are stable for longer time.

.. code-block:: python
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Use the same "new line" or "one line" formatting for all, this looks messy

_, tsoap = trj.get_timesoap(
soap_insight=soap,
)
"""
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remove this comment, in my opinion

n_jobs=4, # Adjust n_jobs according to your computer capabilities
)
the ``soap`` variable is a :class:`.trajectory.Insight` object that contains the computed ``SOAP`` spectra.
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The*

examples/info_gain/trj_*.npy
tests/systems/coex/.*
tests/systems/.*
.DS_Store
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just delete these off your computer?

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It's macOS; every mac user will have this annoying and useless DS_Store. I would ignore it.

If it's a problem, i'll remove that line

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4 participants