- reduced to one the two-command messages logging 
- removed unnecessary commas and brackets in tuples definition
- removed unused imports
- __str__ definition: changed print to return
- removed unused values attribution ("dummy = ...")
- formatted the code
- repaired bad indentation
- removed dummy returns ("return None")
- removed non-meaning body of baseclasses.optimizer.Localize()

1) 1x1 fluxes

Cycle fluxes are now written in cycle-by-cycle files instead of all into one file. This makes file handling easier. Also, I now 
write the full ensemble of 1x1 fluxes to each file, resulting in much more data. The advantage is that this data can be used to post-
aggregate to any mapping needed, including mapping of the full covariance through the ensemble.

2) State fluxes

State fluxes are also written per cycle for the same reasons.

3) Olson fluxes

mean fluxes as well as the ensemble are aggregated to 240 regions (11*19 Olson ecosystems and 30 Ocean regions)

4) TransCom fluxes

mean fluxes as well as the ensemble are aggregated to 23 regions. This is similar to the dedicated routine to make TransCom
fluxes. The disadvantage is that the dedicated routine uses a staevector-to-transcom mapping which is required to be part of the 
Statevector, whereas this is not needed for the new routine. From the ensemble, aggregated fluxes and TransCom covariance can be constructed.

5) Country fluxes

mean and ensemble fluxes are written for a number of large countries (USA, China, Russia, EU27, Brazil,...). This routine can in principle handle all countries of the World, but small countries get unreliable fluxes because of aggregation errors, and the uncertainty on the parameters on smaller scales.

For the aggregation, several dictionary are used to map from the grid to the aggregate regions. These are pickled to make the routine faster, and provided also through SVN.

This routine now also opens the optimizer.nc file and retrieves the forecast values. Then these are written to
the observation files too for later use.

Note that with the current processing chain, it is expected that the format of observations is ObsPack!

This routine can be used to plot time series and histograms of mole fraction data. It can be used inside a CTDAS job to produce these figs after each step,
or it can be used stand-alone by modifying the last few lines wehere a DaCycle object is created.

A new routine has been added that summarizes the X2 statistics for all time steps, and for all sites. The result is an html table in the analysis/summary folder.

This new analysis script writes a summary of the observations that were sampled to a html file. Note that it currently only works with an ObsPack observation object,
because of the file structure assumed on the input side.

An extra LoadRc is needed in Initialize() because the tm5.rc file we want to use is not necessarily the one that was specified on the command line when starting the cycle. This is the case in a recovery from a crash for instance, where we first replace the da_runtime.rc settings, and then we also need to tell the system that we are using a new tm5.rc file (the one in the run-directory, and not the one on the command line).