The code now tests for the presence of the optimizer file, which is False for the purely forward runs. If not present, some steps related to the forecast variables are skipped.

Minor changes to flow of sample_step and sample_state. Caught a possible problem with the get_initialdata for the ObsOperator, which also needs to happen for an advance step.

Implemented the first outlines of a ForwardPipeline that simply runs TM5 with optimized parameters from another run. To be completed...

reverted to r1186 (the last one from 2012), with changes in observation files from r1192-1194 preserved

Corrected reading in of may_reject and may_localize keys from sites_weights.rc, and option added to include only certain hours of the day (for obspack files containing hourly observations)

called from pipeline as call parameters
*warning* take care about get_covariance, it is called from baseclass's "propagate", whereas this function exists only in the derived class.

a lot of my helper comments, but they will disappear soon.

performed actions discussed in email #2
removed unused __str__ functions
a bit of ordering of the pipeline.py

ct/tools.py (whole file)
tm5/observationoperator.py: WriteRunRc
tools/initexit.py: SubmitSubStep
tools/general.py: CreateLinks, ToDectime, StoreData, RestoreData

got rid of the getid and getversion functions (replaced with direct references to identifiers when used)
did a cleanup to dagridded/statevector

made cleanup in tm5_obsoperator
changed functions names - part 1
removed empty Validate function in obs, as well as its call
removed empty Initialize function in dasystem, as well as its call in pipeline (it's still called in some main functions)

- 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.