#+TITLE: Data analysis toolbox
#+AUTHOR: Sebastian Hofer
#+EMAIL: c705264@laptop
#+DATE: 2012-07-16 Mon
#+DESCRIPTION:
#+KEYWORDS:
#+LANGUAGE: en
#+OPTIONS: H:3 num:t toc:t \n:nil @:t ::t |:t ^:t -:t f:t *:t <:t
#+OPTIONS: TeX:t LaTeX:t skip:nil d:nil todo:t pri:nil tags:not-in-toc
#+INFOJS_OPT: view:nil toc:nil ltoc:t mouse:underline buttons:0 path:http://orgmode.org/org-info.js
#+EXPORT_SELECT_TAGS: export
#+EXPORT_EXCLUDE_TAGS: noexport
#+LINK_UP:
#+LINK_HOME:
#+XSLT:


* classes
** dac
*** properties
quiet
postprocess
prngseed
Ntmax
Ntsim
psdnwin
psdwinfunc
psdoverlap
psdrange
refF
expFs
Fs
postp


** dadac

** daddc

*** datadir (dats)
- format: number format, e.g. 'int16',...
- id: unique, human readable identifier; used to form basedir
- basedir: directory identified by id
- datadir: path to data directory relative to basedir
- subdir: path to specific data set, if multiple sets exist
- dataregex: cell array of patterns determining which data file(s) to read from subdir, e.g. {'*Ch0*','*Ch1*'};
- phaseregex: cell array of pattern determining which phase file(s) to read from subdir,
e.g. {'','*PhasesFittedFullNI*'}; An empty entry '' means no file is read. Note that the order has to match dataregex.


* ess class (outdated)
This class extends the built-in ss class. In addtion to the ss it supports the following properties and methods:
** properties
- l: Process noise input
- w: Process noise matrix
- n: Measurement noise matrix
- m: Noise correlation matrix
** hidden properties
- isnoisy: Switch to specify is the object describes a noisy system
** methods
- ss: Convert ess to ss object. This throws away all additional information.
- append: Concatenation of ess objects without connection
- series: Series connect ess objects
Current restrictions
- The number of I/O ports have to match!
- The connections are made in pairs {output_i,input_i}
- The noise model is only correctly connected if it is comple
i.e. sys.isnoisy() == true. Connections of noisy to
deterministic systems (or vice versa) are possible however.
- display: Pretty printing for ess objects
-...

* OM model (outdated)
** parameter structure
structures created by parameters.m from one of the data directories (basedir)
- optomechanics (om):
This holds parameters describing the optomechanical system. Note
that all entries should be properly scaled, i.e. one frequency
should be 1 or 2π. This structure is only needed for creating the
model, so it has to contain all parameters in the model
- phase noise (pn):
In this structure array, each element corresponds to one phase-noise
process. Note that the scaling of the parameters must match the om
structure! The phase-noise model requires the following parameters:
- toggle: toggle phase noise process on/off
- om: phase noise centrail frequency
- ga: phase noise width
- gnc: phase noise coupling cool
- gnl: phase noise coupling lock
- Ga: phase noise magnitude
- experimental (exp):
This holds parameters in physical (SI) units, e.g. Hz, V. This
structure must at least contain the following fields
- Fs: sample rate in Hz
- fm: mechanical frequency in Hz
- omm: mechanical frequency in rad/s
- voltagegain: data scaling factor in V
- voltageoffset: data offset in V
- simulation (sim): rescaled units for simulation (scaling must match om, pn)
- Fs: rescaled sample rate
- dt: timestep in units of rescaled time
- post processing (pp)
- method: design method of filter, e.g. 'ellip','butter',...
- fdesign: fdesign object


* Literature
- Robert Stengel, Optimal Control and Estimation, 1st edition, Dover, 1994
- Monson Hayes, Statistical Digital Signal Processing and Modeling, 1st edition, Wiley, 1996
- Richard Shiavi, Applied Statistical Signal Analysis, 3rd edition, Academic Press, 2007
- Paul McNelis, Neural Networks in Finance, Academic Press, 1st edition, 2005
- F. van der Heijden et al., Classification, Parameter Estimation and State Estimation, 1st edition, Wiley, 2004
- Dan Simon, Optimal State Estimation, 1st edition, Wiley, 2006
- Peter Maybeck, Stochastic Models, Estimation and Control Vol. I-III, Academic Press
- Chi-Tsong Chen, Linear System Theory and Design, Oxford, 1998


* Files

#+name:<<filelist>>
#+begin_src sh :export results
ls *
#+end_src

#+RESULTS: <<filelist>>
| README.org |
| README.txt |
| startup.m |
| |
| analysis: |
| analyze_correlations.m |
| analyzecorr.m |
| comparespec.m |
| compare_spectra.m |
| det_input.m |
| driven_system.m |
| intracavest.m |
| simcorr.m |
| simulate_correlations.m |
| |
| asorted: |
| analyze_correlations_output.m |
| analyze_data_old2.m |
| analyze_data_old.m |
| analyze_data_rest.m |
| bandpass_elliptic_filter.m |
| compare_spectra.m |
| default_parameters.m |
| default_parameters_old.m |
| periodogram_test.m |
| simulate_correlations_output.m |
| |
| @dac: |
| dac.m |
| |
| @dadac: |
| calibrate.m |
| corrcoef.m |
| cov.m |
| ctranspose.m |
| dadac.m |
| mean.m |
| minus.m |
| mldivide.m |
| mrdivide.m |
| mtimes.m |
| plot.m |
| plus.m |
| postprocess.m |
| pwelch.m |
| pwelchplot.m |
| semilogy.m |
| setXData.m |
| setYData.m |
| transpose.m |
| var.m |
| |
| @daddc: |
| daddc.m |
| loadconfig.m |
| load.m |
| |
| @ess: |
| append.m |
| c2d.m |
| checkdim.m |
| compactify.m |
| d2c.m |
| d2d.m |
| dfss.m |
| display.m |
| essdata.m |
| ess.m |
| gennoise.m |
| informationfilter.m |
| initial.m |
| isphysmeas.m |
| kalmanfilter.m |
| kalman.m |
| kfsscov.m |
| kfss.m |
| loadobj.m |
| lsim.m |
| prescale.m |
| prinit.m |
| pwelch.m |
| selectIO.m |
| series.m |
| ss.m |
| |
| figures: |
| sv_expdata_20121027 |
| sv_simulation_2012104 |
| |
| gui: |
| selectcoords.m |
| |
| kalmanfilter: |
| archive |
| informationfilter.m |
| kalmanfilter.m |
| prinit.m |
| |
| misc: |
| bosedist.m |
| checkdim.m |
| dfilt2ess.m |
| dtsize.m |
| fsize.m |
| isphyscov.m |
| lorentzfit.m |
| noop.m |
| nssv.m |
| svdsigned.m |
| symform.m |
| varargs2cell.m |
| varargs2struct.m |
| |
| models: |
| bandpass_model.m |
| beamsplitter_model.m |
| experiment_model.m |
| harmonicoscillator_model.m |
| homodynedetection_model.m |
| omcavity_model.m |
| phasenoiselorentz_model.m |
| thermalbath_model.m |
| |
| saves: |
| bandpass.fda |
| clNoise.sid |
| highpass.fda |
| n4s4dLong_SNlock.mat |
| n4s4_SNlock.mat |
| |
| statisticstests: |
| archive |
| neestest.m |
| nistest.m |
| whitnesstest.m |
| |
| stochasticintegration: |
| archive |
| discretize.m |
| simgauss.m |
| simlinsys.m |
| |
| tests: |
| detfilter_test.m |
| kalmanfilter_test.m |
| kfcomparison_test.m |
| montecarlo_test.m |
| qfactor_test.m |
| simulation_test.m |
| whitenesstest_test.m |
| |
| thirdparty: |
| DataHash.m |
| ringdown.m |
| Singleton.m |

README
======

Author: Sebastian Hofer
Date: 2012-07-16 Mon



Table of Contents
=================
1 Files
2 Literature


1 Files
========

filelist(INVISIBLE)


ls *


README
asorted:
analyze_data_binning_batch.m
analyze_data_binning.m
chi2conf.m
chi2test.m
chi2testtest.m
corr_lag_diag.m
corr_lag_matrix.m
corrtest.m
highpasstest.m
kalman_ho.m
kalman_om.m
kolmtest.m
mycorr.m
periodigram.m
periodogramtest.m
power_spectrum.m
svd_batch.m
svd_g.m
test_analyze_data.m
auto:
README.el
kalmanfilter:
kalmanfilter.m
kalmansmoother_complementary_constant.m
kalmansmoother_complementary_timedependent_measurement.m
kalmansmoother_correlated.m
kalmansmoother_forward_backward_forced.m
kalmansmoother_forward_backward_forced_timedependent.m
kalmansmoother_forward_backward.m
minimaxfilter.m
misc:
check_model_dimensions.m
covnormalized.m
is_physical.m
svd_signed.m
symplectic_form.m
plots:
sv_phases_linear.fig
sv_phases_lti2.fig
stochasticintegration:
discretize_system.m
sample_continuous_lti.m
sample_discrete_linear.m
sample_discrete_lti2.m
sample_discrete_lti.m
sample_gaussian_process.m

2 Literature
=============
- Robert Stengel, Optimal Control and Estimation, 1st edition, Dover, 1994
- Monson Hayes, Statistical Digital Signal Processing and Modeling, 1st edition, Wiley, 1996
- Richard Shiavi, Applied Statistical Signal Analysis, 3rd edition, Academic Press, 2007
- Paul McNelis, Neural Networks in Finance, Academic Press, 1st edition, 2005
- F. van der Heijden et al., Classification, Parameter Estimation and State Estimation, 1st edition, Wiley, 2004
- Dan Simon, Optimal State Estimation, 1st edition, Wiley, 2006
- Peter Maybeck, Stochastic Models, Estimation and Control Vol. I-III, Academic Press

%%%%%%%%%%%%%%%%%%
%%% EVALUATION %%%
%%%%%%%%%%%%%%%%%%
test_dual_homo_measurement('setup_meas.m')

%%%% get_model(pwd, 0.2E-6, 'export')

run_entanglement_evaluation_simulation(pwd, 'test')


% to not export plots (error-prone...)
run_entanglement_evaluation_simulation(pwd, 'test', 'noplots')


% if it works:
[ev, model] = run_entanglement_evaluation_simulation(pwd)

% continue from there..