Initial Commit
Copied across modified scripts and executables from a testing repository

The window factor can be specified for CIJ in 'script_setup_analysis.py'.

The window factors for X and G are hardwired in 'x_pklX.py' and 'x_G.py', respectively.

The '--strong_signal' option (hence the output file name) needs to be changed
in 'x_postanalyse.py' manually.

These setup files and executables, as they are now, are set for analysis with a hanning window.

	modified:   pipeline/executables/x_G.py
	modified:   pipeline/executables/x_S.py
	modified:   pipeline/executables/x_analyse.py
	modified:   pipeline/executables/x_analyse_test.py
	modified:   pipeline/executables/x_pklTStoPSD.py
	modified:   pipeline/executables/x_pklX.py
	modified:   pipeline/executables/x_postanalyse.py
	modified:   pipeline/executables/x_stdP.py
	new file:   pipeline/executables/x_temp_optimals.py
	modified:   pipeline/scripts/script_setup_analysis.py
	modified:   pipeline/scripts/script_setup_post_analysis.py
Included correct window factor in x_S.py (also hardwired it for Hanning)
Included option-parsed window factor in 'x_pklX.py'.

'x_pklX.py' now uses options.window to calculate the window array, and hence the window
factor.  If it's 'None', it is a top-hat window (no window), np.ones( N ); if it is 'hanning',
it is a Hanning window, np.hanning( N )
'window' options added/used to X, G and S computation.

The option 'window' is added to 'x_G.py' and 'x_S.py'.
This option indicates whether no window or a hanning window is used
on the time-series.  The value of the window is then computed for each sample
of the time-series, and then the corresponding windowing factor is computed
for the estimators of X, G and S. ( For X, this window option is parsed to AS.XXX(AS.TimeSeries())
which calculates the windowing for the cross-spectra data cIJ. )
Parsed 'window' option to X, G and S computation in 'x_analyse.py'.

In 'x_analyse.py', setup['X']['window'] (which is specified in 'script_setup_analysis.py')
is parsed to the window option in 'x_pklX.py', 'x_G.py' and 'x_S.py'.
.py~ commit
Enabled 'signal limit' option for stdP and stdPP.

In 'script_setup_postanalyse.py', the dictionaries 'stdP' and 'stdPP' now
have key 'signal limit' that indicates if the sigmas will be computed for
the weak signal limit, or for the strong signal limit, or for both.

In 'x_postanalyse.py', 'signal limit' key is parsed as an option of 'x_stdPP.py'
and 'x_stdP.py'.  For the output file name, if it is for weak-signal limit,
it has no additional tag.  If it is for the strong-signal limit, it has '_strong'.

In 'x_temp_optimals.py', the names of the files for the sigmas have been edited
according to the above.
Tell analyses to use executables from this repository

In 'script_setup_analysis.py' and 'script_setup_postanalysis.py',
the key 'execdir' has been set to 'pipeline/executables' in this git
repository.
Merge branch 'strong_weak_stdP'
Corrected some minor syntax errors
Added print f, df in TimeSeries.winfft()
Removed print f , df statements from TimeSeries.winfft()
Inserted 'print f' statements for X, G and S computation.

Statements have been inserted in AS.XXX(), AS.GGG() and 'AS.SSS()'
to print out the lowest frequency, the cadence and the highest frequency
of the frequency-dependent variables, and their corresponding
coarse frequency.

	modified:   modules/AnisotropySearch.py
	modified:   modules/AnisotropySearch.pyc
	modified:   modules/AnisotropySearch.py~
Rewritten AS.coarsefrequency() using a revised and more clear algorithm
Updated 'script_sphericalharmonic.py' to used latest modules

Script for generating Plm's for some SpH sky distribution,
'script_sphericalharmonic.py' updated to use the most recent modules.
Added line to print fNyq's  near beginning of x_P_to_TS.py
Commented out the fNyq == (options.Nf+1)*options.df check

This does not work because the floating number options.df is rounded up
when parsed as an option of optparse.OptionParser, so in general, it does
not match fNyq, even though the actual (Nf+1)*df == Nfyq.

	modified:   x_P_to_TS.py
	modified:   x_P_to_TS.py~
Make 'x_P_to_TS.py' read non-TDI observables

This is because the glms used for testing are not necessary saved in
directories named after TDI observables.

	modified:   x_P_to_TS.py
	modified:   x_P_to_TS.py~
Made 'x_P_to_TS.py' to read 'orf-dXXX.pkl' files from directory '/data'

	modified:   executables/x_P_to_TS.py
	modified:   executables/x_P_to_TS.py~