User now controls the name of the csv output dir.

* timestream/manipulate/pipecomponents.py (ResultFeatureWriter_csv): We use
outputPathPrefix of the context to set the prefix for the csv output dir.
* util/pipeline_demo.py (outputPrefix): is the config var that contains the
preferred name of the output basename dir.
(outputPath): Directory where resulting directories will be put
(outputPathPrefix):  Convenience var outputPath/outputPrefix. Where outputPrefix
will identify all the outputs from this run in that directory.
Prepend the prefix name to the output statistic files.

Use outputPrefix in the csv statistic file name.
* timestream/manipulate/pipecomponents.py: Use context.outputPrefix val
* util/pipeline_demo.py: Create a context.outputPrefix value
Allow user to choose type of timestamp

We default to %Y_%m_%d_%H_%M_%S_%02d.
* timestream/manipulate/pipecomponents.py (ResultFeatureWriter_csv.__call__):
Default to calendar format, but allow the user to choose between a custom
format, Linux seconds or Linux milliseconds.
Remove unnecessary warning.

* timestream/__init__.py (TimeStream.load_pickled_image): If we know that the
file does not exists, dont try to upload from pickled file.
Put all feature writers in the same class.

We now call the same component to activate feature writing. We must specify the
type of container for the output. Choices are: csv and ndarray
* timestream/manipulate/pipecomponents.py (ResultingFeatureWriter): New class to
replace *csv and *ndarray.
(ResultingFeatureWriter.csvCheck, ResultingFeatureWriter.csvCall): Will take
care of the csv output.
(ResultingFeatureWriter.ndarrayCheck, ResultingFeatureWriter.ndarrayCall): Will
take care of the ndarray output.
(ResultingFeatureWriter_csv, ResultingFeatureWriter_ndarray): Removed unneeded
classes.
* timestream/manipulate/pipeline.py (complist): Add new "global" class and
previous specific ones.