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NAME:
CONVOLVE_GAUSSIAN_1D
PURPOSE:
Routine convolves scalar or vector field to a given resolution
with a Gaussian kernel
CATEGORY:
Data Processing
CALLING SEQUENCE:
convolve_gaussian_1d,resol,tarr,varrin,varrout
INPUTS:
resol - desired time resolution in seconds
tarr - time array (1D, double, seconds)
varrin - input field - 1D or mD array (ntimepoints,m)
KEYWORDS: none
PARAMETERS: eps - truncate Gaussian at this height
ni - initial length of transform is 2^ni (adjusted depending on data)
ndump - initial length of leakage-dumping tail (adjusted by code)
OUTPUTS:
varrout - output array of the same dimensions that varrin
DEPENDENCIES: None - can be used alone.
MODIFICATION HISTORY:
Written by: Vladimir Kondratovich 2008/10/10.
(See external/developers/outliers_and_convolution/convolve_gaussian_1d.pro)
NAME:
OUTLIERS_AND_CONVOLUTION_CRIB
PURPOSE:
Crib sheet showing the use and work of the outlier removal and
convolution routines.
CATEGORY:
Crib sheet
CALLING SEQUENCE:
crib_outliers_and_convolution
INPUTS:
none; the code prompts user to continue by entering .continue command
KEYWORDS:
none
PARAMETERS: 3 parameters for outlier filtering and convolution are described
and set in the code. Another parameter is set in the auxillary
routine remove_outliers_repair.pro
OUTPUTS:
graphics
DEPENDENCIES: convolve_gaussian_1d.pro, remove_outliers.pro, remove_outliers_repair.pro,
wi_swe_load.pro, get_data.pro, xclip.pro, xdegap.pro, xdeflag.pro.
MODIFICATION HISTORY:
Written by: Vladimir Kondratovich 2007/12/28.
(See external/developers/outliers_and_convolution/outliers_and_convolution_crib.pro)
NAME:
REMOVE_OUTLIERS
PURPOSE:
Routine eliminates outliers. Quadratic trend is determined in a hollow
vicinity of each point. The data value is compared with the trend
value. If the deviation is statistically improbable, the value is
repaired. There are 6 options for repair to be set in the subroutine
remove_outliers_repair.pro. Routine gives the summary of its work: how
many of the total number of numeric values were repaired, and the number
of failure cases (when it was impossible to establish a trend).
CATEGORY:
Data Processing
CALLING SEQUENCE:
remove_outliers, epoch, valuesin, d, tmax, nmax
INPUTS:
EPOCH: time array for the data values. Any time units may be used,
just do it consistently. Double 1D array.
VALUESIN: 1D array of values to filter; its numerical values are
replaced by filtered data at the end.
D: half-size of the hollow vicinity of the point where trend is
established (integer)
TMAX: maximal time interval covered by the hollow vicinity (double)
NMAX: maximal deviation from the trend deemed to be probable
(in units of standard deviation). Integer.
KEYWORDS: None
PARAMETERS: Repair option set in subroutine remove_outliers_repair.pro.
OUTPUTS:
VALUESIN: Array of filtered values (numerical values of input are replaced).
The code may produce "division by zero" warnings originated in the svdfit
routine. They should be ignored.
DEPENDENCIES: remove_outliers_repair.pro
MODIFICATION HISTORY:
Written by: Vladimir Kondratovich 2007/12/28.
(See external/developers/outliers_and_convolution/remove_outliers.pro)
NAME:
REMOVE_OUTLIERS_REPAIR
PURPOSE:
Routine repairs outliers. Quadratic trend is determined in a hollow
vicinity of each point. The data value is compared with the trend
value. If the deviation is statistically improbable, the value is
repaired. There are 6 options for repair.
CATEGORY:
Data Processing
CALLING SEQUENCE:
repair, valneib, tneib, valiin, nmax, valiout
INPUTS:
VALNEIB: array of the data values in the hollow vicinity of the point.
TNEIB: array of the observation times for the above values.
VALIIN: the value to filter.
NMAX: maximal probable deviation from the average in units of standard
deviation
KEYWORDS: None
PARAMETERS: The code has one parameter "sch" setting the way outlier is
repaired.
OUTPUTS:
VALIOUT: filtered value.
DEPENDENCIES: None. Called by remove_outliers.pro
MODIFICATION HISTORY:
Written by: Vladimir Kondratovich 2007/12/28.
(See external/developers/outliers_and_convolution/remove_outliers_repair.pro)