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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)