MacroG16-train&data:
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This example should be run once you understand how to prepare and analyze image
coming from a digital camera (see MacroG16-example). This is a collection
of 12 samples of zooplankton from Tulear, Madagascar. All these samples were
collected in a single station (South Pass of Tulear reef) and form a short time
series monitoring plankton during one year.

There is also an example of an unrefined manual training sets provided in
the _train-detailed subdirectory. It is just to illustrate the process and you
are supposed to play a little bit with it (make other groups, merge those who
are not easily separated, etc. before you can arrive at a satisfactory solution
for this kind of data. This is done by exploring and reworking the training set
under XnView (Apps -> Image viewer (XnView)). Once modified, reimport it and
try again the different classification algorithms. One possible modification to
test is to pool lateral and dorsal subgroups done in the detailed training set
(these are obviously artificial subgroups corresponding to the random position
taken by the particles on the pictures). An example of a training with a little
bit of reworking (not optimized yet!) is provided in the _train-reworked
subdirectory. 

When you have a satisfactory training set, you can save it for further reuse
(Objects -> Save), and you can process all the 12 samples in this series.

To run this example:
--------------------
- Download and unzip "MacroG16-train&data.zip" in your 'ZooPhytoImage Examples'
  directory, or anywhere you like to place it.
  
- Start Zoo/PhytoImage and import the detailed training set (sixth button).
  Select the _train subdirectory. Importation takes a couple of seconds.
  Wait until you see a table of summary statistics about this training set.
  
- Train a classifier with it (seventh button). Experiment with the different
  algorithms provided, and display the "confusion matrix" using the eighth
  button after the training. As you can see, some groups are hard to identify
  with these data. You should rework the groups at this stage.
  
- Train a random forest classifier with the '_train-reworked' training set and
  save it (Objects -> save; to reuse it later, or on another computer, reopen
  the corresponding file with Objects -> load). Inspect its performances. For
  the sake of this tutorial, we will assume that you are satisfied with it.
  
- Now that you have a classifier, you can predict all particles in your 12
  samples, and calculate total/partial abundances, total/partial biomasses and
  total/partial size spectra. Usually, you need to document your series in a
  'description.zis' file (nineth button) before you can process it. In the
  present case, we provide an example description file. Inspect it to learn what
  you are supposed to put in it.
  
- For processing all the 12 samples, click on the tenth button and select the
  provided 'description.zis' file. Each sample is processed in turn. If you
  checked the option for saving individual measurements, a table for each sample
  with the ECD, identification and calculation of individual biomass of each
  particle is saved on the same directory as the one where the 'description.zis'
  file resides. Once this is done, you can visualize the results by clicking on
  the eleventh button. For instance, you could make a composite graph by
  selecting 'Abd total', 'Abd Calanoida', 'Abd Chaetognatha', 'Abd Egg - round',
  'spectrum of MTPS.2005-10-30.H1' and 'spectrum of MTPS.2005-12-01.H1' which
  shows some interesting variations in the series.
  
- Finally, you can further analyse your series by using all the statistical or
  graphical tools available in R. See Help -> Manuals -> An Introduction to R
  from the 'R Console' menu, if you are not familiar with R. All the data is in
  a 'ZIRes' object (called 'ZIres' by default), and it is a 'data frame' in R's
  terminology. Since this is the basic component in R, you can easily use any
  function (like plot(), for instance) on it to continue your analysis with R.

- If you prefer to use another software for you analysis (Matlab? Excel? other?)
  you just need to export your data (twelveth button). Data are exported as tab
  separated ASCII files, a format easily readable by any external software.