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<p>Thanks, Jiri</p>
<p><br>
</p>
<p>Again let's clarify what we think. see in text<br>
</p>
<br>
<div class="moz-cite-prefix">Le 13/07/2017 à 16:03, Jiří Nádvorník a
écrit :<br>
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<p class="MsoNormal"><span>Hi all,</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Thank you for nice summary, Francois.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>My comments inline.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Cheers,</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Jiri </span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span> </span></p>
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<p class="MsoNormal"><b><span>From:</span></b><span>
<a class="moz-txt-link-abbreviated" href="mailto:dm-bounces@ivoa.net">dm-bounces@ivoa.net</a> [<a class="moz-txt-link-freetext" href="mailto:dm-bounces@ivoa.net">mailto:dm-bounces@ivoa.net</a>] <b>On
Behalf Of </b>François Bonnarel<br>
<b>Sent:</b> Wednesday, July 12, 2017 6:11 PM<br>
<b>To:</b> <a class="moz-txt-link-abbreviated" href="mailto:mireille.louys@unistra.fr">mireille.louys@unistra.fr</a>; <a class="moz-txt-link-abbreviated" href="mailto:dm@ivoa.net">dm@ivoa.net</a>;
<a class="moz-txt-link-abbreviated" href="mailto:voevent@ivoa.net">voevent@ivoa.net</a>; <a class="moz-txt-link-abbreviated" href="mailto:dal@ivoa.net">dal@ivoa.net</a><br>
<b>Subject:</b> [timeDomain: model for Time series]
discussion on Timeseries Data model Note / ND-point
.... or not ??? !!!</span></p>
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<p>Dear all,</p>
<p>This is a follow-up of these two emails</p>
<p><a moz-do-not-send="true"
href="http://mail.ivoa.net/pipermail/dm/2017-July/005583.html">http://mail.ivoa.net/pipermail/dm/2017-July/005583.html</a></p>
<p>and</p>
<p><a moz-do-not-send="true"
href="http://mail.ivoa.net/pipermail/dm/2017-July/005594.html">http://mail.ivoa.net/pipermail/dm/2017-July/005594.html</a></p>
<p>which are discussing chapter 4 of TimeSeries Data Model
IVOA note within the scope of the TimeDomain effort
summarized by Ada there:</p>
<p><a moz-do-not-send="true"
href="http://mail.ivoa.net/pipermail/dm/2017-July/005581.html">http://mail.ivoa.net/pipermail/dm/2017-July/005581.html</a></p>
<p class="MsoNormal">Discussed issue: do we need ND-points or
not ?<br>
<br>
Reference : Laurent's diagram in email (<a
moz-do-not-send="true"
href="http://mail.ivoa.net/pipermail/dm/2017-July/005581.html">http://mail.ivoa.net/pipermail/dm/2017-July/005581.html</a>)
and CubeDM draft from <a moz-do-not-send="true"
href="http://volute.g-vo.org/svn/trunk/projects/dm/CubeDM-1.0/doc/WD-CubeDM-1.0-20170203.pdf">http://volute.g-vo.org/svn/trunk/projects/dm/CubeDM-1.0/doc/WD-CubeDM-1.0-20170203.pdf</a>
(last version)<br>
<br>
In figure 4 of Jiri's note, SparseCube doesn't relate to the
ND-point class as it is the case in the IVOA sparse cube
data model project.<span></span></p>
<p class="MsoNormal"><b><i><span>[[Jiri Nadvornik]] The
problem we had with the ND-point is that it holds
complete metadata for each individual point of the
time series, so also the statistical distribution
would need to go here (which is not really related to
a point, but rather to an axis). And the metadata
about spectral points or photometry points can be kept
in the Spectral DM or Photometry DM metadata, so in
the end we realized that the ND-point class was empty.</span></i></b><br>
</p>
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<i><b>Again, I'm trying to start from what is in the CubeDM draft.
In my understanding ND-point doesn't seem to contain any axis
metadata and is only gathering a set of individual values
contained in the DataAxis instances. If we have a "tabular
vision" of our TimeSeries ND-point is modeling the content of a
row. In an ND-Point instnace we will have the Time DataAxis
value and any other corresponding DataAxis values : Flux, magnitude
, Position + velocity, etc... </b></i><br>
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<p class="MsoNormal"><br>
The ND-point class gathers several DataAxis (or Observable)
containers for measurements on a given data Axis to
represent a "point" or "event" in the data space.<span></span></p>
<p class="MsoNormal"><b><i><span>[[Jiri Nadvornik]] My
understanding here was that it gathers several one
ND-point gathers 1 *point* from each DataAxis, not the
whole DataAxis – can somebody please verify one or the
other?</span></i></b><br>
</p>
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<i><b>Yes ND-point gather 1 value from each DataAxis</b></i><br>
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<p class="MsoNormal"><br>
On Jiri's figure a set of "CubeAxis" is directly related to
"SparseCube". This doesn't imply explicitly a relationship
between each of these "CubeAxis" instances and doesn't even
imply that each of these "CubeAxis" will have the same
number of instances.<span></span></p>
<p class="MsoNormal"><b><i><span>[[Jiri Nadvornik]] Correct.
We did not consider relationships between individual
axes. While photometry axis will usually have the same
amout of points as the time axis, we will have only
several bands on the spectral axis. Can also the
spatial axis have fewer elements than the time axis (I
don’t have different spatial coordinates for every
point of the light curve)?</span></i></b><br>
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<i><b>Yes you may not have for coordinates. But at least one the
other DataAxis than time should be sampled and dependant from
time. Actually ND-point is modeling this dependance</b></i><br>
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<p class="MsoNormal"><br>
So I am wondering if we should not reintroduce the ND-point
feature between "SparseCube" and "CubeAxis" or "DataAxis".
What do you think ?<span></span></p>
<p class="MsoNormal"><b><i><span>[[Jiri Nadvornik]] My
suggestion would be to have the Cube DM hold metadata
for axis statistical distribution</span></i></b></p>
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<i><b>ObsDataSet and SparseCubeDataset allready have a lot of
general metadata (for example the characterisation is part of
ObsDataSet, and the cordinate Mapping and System is in
SparseCubeDataSet. If we need per axis statistics it should be
added somewhere there, I guess<br>
<br>
Cheers<br>
François<br>
</b></i>
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<p class="MsoNormal"><b><i><span> (see Quantity class in
TimeSeriesCube UML - <a moz-do-not-send="true"
href="https://volute.g-vo.org/viewvc/volute/trunk/projects/time-domain/time-series/time-series-cube/ivoa-note-1.0/"><span>https://volute.g-vo.org/viewvc/volute/trunk/projects/time-domain/time-series/time-series-cube/ivoa-note-1.0/</span></a>
) and the metadata about the measurements in what we
already have – Photometry DM, spatial and time in STC,
Spectral DM… but both types of metadata separately for
each axis, not for one ND-Point class.</span></i></b></p>
<p class="MsoNormal"><br>
<br>
François (after discussions with Laurent, Mireille and Ada
on this topic)<br>
<br>
</p>
<pre> </pre>
<pre> </pre>
<pre> </pre>
<p class="MsoNormal"><br>
<br>
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<div>
<p class="MsoNormal">Le 12/07/2017 à 10:57, Mireille Louys a
écrit :</p>
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<p class="MsoNormal"><span>Dear DM and Time Domain
followers,</span> <br>
<br>
<span>I am trying, together with my CDS colleagues, to
recap on the various DMs available in the IVOA and
understand the possible links between the future Time
Series Model ( as sketched in Jiris's Note) and existing
DMs like ND-Cube and STC 2.<br>
<br>
Here is a graph proposed by Laurent Michel to clarify
the links in 3 main parts : </span></p>
<ul type="disc">
<li class="MsoNormal"><i><span>DataSetMetadata DM</span></i><span>,
which has the main ObsDataset Class ,</span></li>
<li class="MsoNormal"><i><span>ND-CubeDM</span></i><span>,
which defines a SparseCubedataset</span></li>
<li class="MsoNormal"><i><span>TimeSerieCubeDM</span></i><span>,
which highlights the special properties of a Cube
depending on a Time axis</span></li>
</ul>
<p class="MsoNormal"><span>I think this is essential to
highlight the inheritance path between these 3 DM
building blocks: <br>
a TimeSeriesCube <is a >
NDCubeDM::SparseCubeDataset<br>
</span>a <span>NDCubeDM::SparseCubeDataset <is a >
DatasetMetadaDM::ObsDataset<br>
</span><br>
<span>ObsDataset has a <i>dataproduct_type</i> attribute
which allows to discover all dataproducts of type '
timeseries'. <br>
this provides the container object for time-dependent
data.<br>
<br>
If we need to select <i>timeseries dataproducts</i>
according to some properties extracted from their data
we can:<br>
- reuse what Obscore DM provides to explain general
axes properties<br>
target_name, s_region, s_resol, t_min, t_max, t_resol,
em_min, em_max, em_resol, etc. are the basic properties
for discovery<br>
<br>
- provide a richer description of the TimeAxis and
ObservableAxis. <br>
For that , extracting a statistical profile from the
data contained in the Cube could do the job. <br>
this means to access and analyse the Data part in
ND-Cube , i. e the ND-Points gathered in a SparseCube
Object<br>
<br>
I guess more properties can be exposed to qualify the
axes present in the Timeseries dataset , but for the
moment , I see some overlap of notions between <br>
CharacterisationDM::ObservableAxis,
STC2.0::CoordMeasurement (??) and
TimeSerieCubeDM::CubeAxis.<br>
<br>
This would be great if we could sort this out, <br>
but currently , I would appreciate your feedback on the
attached diagram , in order to proceed on the data model
structure. <br>
<br>
Cheers, Mireille ( after discussions together with
Laurent, François, Ada) <br>
<br>
<br>
</span></p>
<pre>-- </pre>
<pre>--</pre>
<pre>Mireille Louys</pre>
<pre>CDS Laboratoire Icube </pre>
<pre>Observatoire de Strasbourg Telecom Physique Strasbourg</pre>
<pre>11 rue de l'Université 300, Bd Sebastien Brandt CS 10413 </pre>
<pre>F- 67000-STRASBOURG F-67412 ILLKIRCH Cedex</pre>
<pre>tel: +33 3 68 85 24 34</pre>
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