Python tools for retrieving DIII-D bi-color interferometer (bci) signals.

Package management is cleanly handled on Iris via modules (GA internal site). The bci package has a corresponding modulefile here.

To use the bci package, change to the directory you'd like to download the source files to and retrieve the source files from github by typing

$ git clone https://github.com/emd/bci.git

The created bci directory defines the package's top-level directory. The modulefiles should be similarly cloned.

Now, at the top of the corresponding modulefile, there is a TCL variable named bci_root; this must be altered to point at the top-level directory of the cloned bci package. That's it! You shouldn't need to change anything else in the modulefile. The bci module can then be loaded, unloaded, etc., as is discussed in the above-linked Iris documentation.

The modulefile also defines a series of automated tests for the bci package. Run these tests at the command line by typing

$ test_bci

If the tests return "OK", the installation should be working. (Currently, no automated tests are implemented).

Define an environmental variable $bci_path specifying the appropriate MDSplus server's tree-path definitions by, for example, adding the following to your .bashrc

$ export bci_path='atlas.gat.com::/data/usershots/~t\;atlas.gat.com::/data/shots/~t/~f~e/~d~c\;atlas.gat.com::/data/orphans/\;atlas.gat.com::/data/models/~t'

Now, change to the directory you'd like to download the source files to and retrieve the source files from github by typing

$ git clone https://github.com/emd/bci.git

Change into the bci top-level directory by typing

$ cd bci

For accounts with root access, install by running

$ python setup.py install

For accounts without root access (e.g. a standard account on GA's Venus cluster), install locally by running

$ python setup.py install --user

To test your installation, run

$ nosetests tests/

If the tests return "OK", the installation should be working.

The single-pass phase measured by the DIII-D bi-color interferometer (BCI) can be readily retrieved via the the bci.signal.Signal class. For example, use:

import bci

shot = 169572
tlim = [0, 2]  # [tlim] = s

sig_V2 = bci.signal.Signal(shot, chord='V2', beam='CO2', tlim=tlim)

to retrieve the phase signal from the V2 CO2 beam. Valid values of chord are {'V1', 'V2', 'V3', 'R0'} and valid values of beam are {'CO2', 'HeNe'}. If beam is 'CO2', the vibration_subtracted keyword can also be set to True to remove vibrational contributions to the CO2-measured phase; that is

sig_V2_no_vib = bci.signal.Signal(
    shot, chord='V2', beam='CO2', tlim=tlim,
    vibration_subtracted=True)

Vibrational contributions to the CO2-measured phase are typically "small" for frequencies above 10 kHz. While removing the vibrational contributions from the CO2-measured phase can increase the signal-to-noise ratio in some cases, it can potentially introduce other problems, as is discussed on the BCI homepage (GA internal site).

The autospectral density of V2-measured phase can then be computed and easily visualized using the random_data package. Specifically,

import random_data as rd

# Spectral-estimation parameters
Tens = 5e-3         # Ensemble time length, [Tens] = s
Nreal_per_ens = 10  # Number of realizations per ensemeble

# Compute autospectral density
asd_V2 = rd.spectra.AutoSpectralDensity(
    sig_V2.x, Fs=sig_V2.Fs, t0=sig_V2.t0,
    Tens=Tens, Nreal_per_ens=Nreal_per_ens)

asd_V2.plotSpectralDensity(
    flim=[50e3, 400e3],
    xlabel='$t \, [\mathrm{s}]$',
    ylabel='$f \, [\mathrm{Hz}]$')