User Defined Preprocessing - Revision history

imported>Donal at 01:33, 23 December 2017

2017-12-23T01:33:08Z

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	__TOC__		__TOC__
	Each preprocessing method available in the <tt>[[preprocess\|preprocess.m]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser\|preprouser.m]]</tt> file or in a Matlab binary <tt>preprocatalog.mat</tt> file. The methods defined in these three files are available to all tools making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. See also [[#Adding_Custom_Methods_to_Solo\|Adding Custom Methods to Solo (below)]]		Each preprocessing method available in the <tt>[[preprocess\|preprocess.m]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser\|preprouser.m]]</tt> file or in a Matlab binary <tt>preprocatalog.mat</tt> file. The methods defined in these three files are available to all tools making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. See also [[#Adding_Custom_Methods_to_Solo\|Adding Custom Methods to Solo (below)]]

			Please note that the standard preprocessing methods cannot change the dimension of the data being processed, nor the include fields if it is a dataset. This is a PLS_Toolbox convention which custom user-defined preprocessing methods must also follow.

	The fields in a preprocessing structure include:		The fields in a preprocessing structure include:

imported>Jeremy at 23:46, 3 February 2012

2012-02-03T23:46:03Z

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	pp.keyword = 'Normalize';		pp.keyword = 'Normalize';
	pp.userdata = 2;</pre>		pp.userdata = 2;</pre>
	The preprocessing structure used for ~~Savitsky~~-Golay smoothing and derivatives is shown below. In many ways, this structure is similar to the sample normalization structure, except that <tt>savgol</tt> takes a DataSet Object as input and, thus, <tt>usesdataset</tt> is set to 1. Also note that because of the various settings required by <tt>savgol</tt>, this method makes use of the <tt>settingsonadd</tt> feature to bring up the settings GUI as soon as the method is added.		The preprocessing structure used for Savitzky-Golay smoothing and derivatives is shown below. In many ways, this structure is similar to the sample normalization structure, except that <tt>savgol</tt> takes a DataSet Object as input and, thus, <tt>usesdataset</tt> is set to 1. Also note that because of the various settings required by <tt>savgol</tt>, this method makes use of the <tt>settingsonadd</tt> feature to bring up the settings GUI as soon as the method is added.

	<pre>pp.description = 'SG Smooth/Derivative';		<pre>pp.description = 'SG Smooth/Derivative';

imported>Jeremy: /* Detailed Field Descriptions */

2011-11-09T18:31:04Z

Detailed Field Descriptions

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	:<pre>pp.usesdataset = 0;</pre>		:<pre>pp.usesdataset = 0;</pre>

	* <tt>caloutputs</tt>: Contains the number of values ~~expected~~ in field out if a calibrate operation has been performed, for functions which require a <tt>calibrate</tt> operation prior to an apply or undo. For example, in the case of mean centering, the mean values stored in the field out are required to apply or undo the operation. Initially, out is an empty cell ({}) but following the calibration, it becomes a single-item cell (length of one). By examining this length, <tt>preprocess</tt> can determine if a preprocessing structure contains calibration information. The caloutputs field, when greater than zero, indicates to preprocess that it should test the <tt>out</tt> field prior to attempting an apply or undo.		* <tt>caloutputs</tt>: Contains the number of values required in field <tt>out</tt> once a calibrate operation has been performed. This must be set for functions which require a <tt>calibrate</tt> operation prior to an apply or undo. For example, in the case of mean centering, the mean values stored in the field out are required to apply or undo the operation. Initially, out is an empty cell ({}) but following the calibration, it becomes a single-item cell (length of one). By examining this length, <tt>preprocess</tt> can determine if a preprocessing structure contains calibration information. The caloutputs field, when greater than zero, indicates to preprocess that it should test the <tt>out</tt> field prior to attempting an apply or undo. A value of zero for <tt>caloutputs</tt> also indicates that the method is a row-wise-only method and that the results of preprocessing any row are completely independent of the other rows. This is used by some functions to indicate that the corresponding preprocessing can be done in advance of some iterative processing (e.g. cross-validation.)
	:Example:		:Example:
	:In the case of mean-centering, the length of out should be 1 (one) after calibration:		:In the case of mean-centering, the length of out should be 1 (one) after calibration:

imported>Jeremy at 19:19, 3 November 2011

2011-11-03T19:19:36Z

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 __TOC__
-Each preprocessing method available in the <tt>[[preprocess|preprocess.m]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser|preprouser.m]]</tt> file or in a Matlab binary <tt>preprocatalog.mat</tt> file. The methods defined in these three files are available to all tools making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog.
+Each preprocessing method available in the <tt>[[preprocess|preprocess.m]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser|preprouser.m]]</tt> file or in a Matlab binary <tt>preprocatalog.mat</tt> file. The methods defined in these three files are available to all tools making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. See also [[#Adding_Custom_Methods_to_Solo|Adding Custom Methods to Solo (below)]]
 The fields in a preprocessing structure include:
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 pp.keyword               = 'MSC (mean)';
 pp.userdata             = 1;</pre>
 ==Summary==
 The Preprocessing Structure provides a generic framework within which a user can organize and automate preprocessing with PLS_Toolbox. Both MATLAB structure and cell data types are used extensively in the Preprocessing Structure. Any custom preprocessing you define yourself can be combined with the numerous methods provided by PLS_Toolbox in order to meet your analysis needs.

imported>Jeremy at 17:56, 3 November 2011

2011-11-03T17:56:07Z

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 __TOC__
-Each preprocessing method available in the <tt>[[preprocess]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser]]</tt> file. The methods defined in these two files are available to all functions making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. The fields in a preprocessing structure include:
+Each preprocessing method available in the <tt>[[preprocess|preprocess.m]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser|preprouser.m]]</tt> file or in a Matlab binary <tt>preprocatalog.mat</tt> file. The methods defined in these three files are available to all tools making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog.
 * <tt>description</tt> - textual description of the particular method.

imported>Jeremy at 16:58, 20 December 2010

2010-12-20T16:58:07Z

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	__TOC__		__TOC__
	Each preprocessing method available in the <tt>preprocess</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>preprouser</tt> file. The methods defined in these two files are available to all functions making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. The fields in a preprocessing structure include:		Each preprocessing method available in the <tt>[[preprocess]]</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>[[preprouser]]</tt> file. The methods defined in these two files are available to all functions making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. The fields in a preprocessing structure include:

	* <tt>description</tt> - textual description of the particular method.		* <tt>description</tt> - textual description of the particular method.

imported>Jeremy at 16:56, 20 December 2010

2010-12-20T16:56:00Z

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	* <tt>caloutputs</tt> - integer: number of expected items in field out after calibration has been performed.		* <tt>caloutputs</tt> - integer: number of expected items in field out after calibration has been performed.
	* <tt>keyword</tt> - text string which users can pass to preprocess to obtain this structure.		* <tt>keyword</tt> - text string which users can pass to preprocess to obtain this structure.
			* <tt>category</tt> - text string which associates this method with other similar methods (for display purposes only).
	* <tt>userdata</tt> - user-defined variable often used to store method options.		* <tt>userdata</tt> - user-defined variable often used to store method options.

imported>Jeremy: /* Detailed Field Descriptions */

2010-12-20T16:55:06Z

Detailed Field Descriptions

imported>Scott at 18:57, 14 October 2008

2008-10-14T18:57:43Z

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			__TOC__
	Each preprocessing method available in the <tt>preprocess</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>preprouser</tt> file. The methods defined in these two files are available to all functions making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. The fields in a preprocessing structure include:		Each preprocessing method available in the <tt>preprocess</tt> function is defined using a preprocessing structure. The standard methods are defined in the <tt>preprocatalog</tt> file, and additional user-defined methods can be defined in the <tt>preprouser</tt> file. The methods defined in these two files are available to all functions making use of the <tt>preprocess</tt> function. See the <tt>preprouser</tt> file for examples of defining and adding methods to the available methods catalog. The fields in a preprocessing structure include:

imported>Scott: /* Detailed Field Descriptions */

2008-10-14T18:57:13Z

Detailed Field Descriptions

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	:<pre>pp.settingsonadd = 1;</pre>		:<pre>pp.settingsonadd = 1;</pre>

	* <~~pre~~>usesdataset</~~pre~~>: Contains a Boolean (1=true, 0=false) value indicating if the method is capable of handling DataSet Objects or can only handle standard MATLAB data types (double, uint8, etc).		* <tt>usesdataset</tt>: Contains a Boolean (1=true, 0=false) value indicating if the method is capable of handling DataSet Objects or can only handle standard MATLAB data types (double, uint8, etc).
	::1 = function can handle DataSet Objects; preprocess will pass entire DataSet Object. It is the responsibility of the function(s) called by the method to appropriately handle the <tt>include</tt> field.		::1 = function can handle DataSet Objects; preprocess will pass entire DataSet Object. It is the responsibility of the function(s) called by the method to appropriately handle the <tt>include</tt> field.
	::0 = function needs standard MATLAB data type; <tt>preprocess</tt> will extract data from the DataSet Object prior to calling this method and reinsert preprocessed data after the method. Although excluded columns are never extracted and excluded rows are not extracted when performing calibration operations, excluded rows are passed when performing apply and undo operations.		::0 = function needs standard MATLAB data type; <tt>preprocess</tt> will extract data from the DataSet Object prior to calling this method and reinsert preprocessed data after the method. Although excluded columns are never extracted and excluded rows are not extracted when performing calibration operations, excluded rows are passed when performing apply and undo operations.
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	:::In savgol several variables are defined with various method options, then they are assembled into userdata:		:::In savgol several variables are defined with various method options, then they are assembled into userdata:
	:::<pre>pp.userdata = [windowsize order derivative];</pre>		:::<pre>pp.userdata = [windowsize order derivative];</pre>

	==Examples==		==Examples==
	The preprocessing structure used for sample normalization is shown below. The <tt>calibrate</tt> and <tt>apply</tt> commands are identical and there is no information that is stored during the calibration phase; thus, <tt>caloutputs</tt> is 0. The order of the normalization is set in <tt>userdata</tt> and is used in both <tt>calibrate</tt> and <tt>apply</tt> steps.		The preprocessing structure used for sample normalization is shown below. The <tt>calibrate</tt> and <tt>apply</tt> commands are identical and there is no information that is stored during the calibration phase; thus, <tt>caloutputs</tt> is 0. The order of the normalization is set in <tt>userdata</tt> and is used in both <tt>calibrate</tt> and <tt>apply</tt> steps.

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	* <tt>calibrate, apply, undo</tt>: Contain a single cell of one or more command strings to be executed by preprocess when performing calibration, apply or undo operations. <tt>Calibrate</tt> actions are done on original calibration data, whereas <tt>apply</tt> is done on new data. The <tt>undo</tt> action is used to remove preprocessing from previously preprocessed data, although it may be undefined for certain methods. If this is the case, the undo string should be an empty cell.		* <tt>calibrate, apply, undo</tt>: Contain a single cell of one or more command strings to be executed by preprocess when performing calibration, apply or undo operations. <tt>Calibrate</tt> actions are done on original calibration data, whereas <tt>apply</tt> is done on new data. The <tt>undo</tt> action is used to remove preprocessing from previously preprocessed data, although it may be undefined for certain methods. If this is the case, the undo string should be an empty cell.

	The command strings should be valid MATLAB commands. Each command will be executed inside the preprocess environment in which the following variables are available:		:The command strings should be valid MATLAB commands. Each command will be executed inside the preprocess environment in which the following variables are available:
	:* <tt>data</tt>: The actual data on which to operate and in which to return modified results. If the field <tt>usesdataset</tt> is 1 (one) then data will be a DataSet Object. Otherwise, data will be a standard MATLAB variable which has been extracted from the DataSet Object. In the latter case, the extracted data will not contain any excluded columns from the original DataSet Object. In addition, in <tt>calibrate</tt> mode rows excluded in the original DataSet Object will not be passed. All rows, excluded or not, are passed in apply and undo modes.		:* <tt>data</tt>: The actual data on which to operate and in which to return modified results. If the field <tt>usesdataset</tt> is 1 (one) then data will be a DataSet Object. Otherwise, data will be a standard MATLAB variable which has been extracted from the DataSet Object. In the latter case, the extracted data will not contain any excluded columns from the original DataSet Object. In addition, in <tt>calibrate</tt> mode rows excluded in the original DataSet Object will not be passed. All rows, excluded or not, are passed in apply and undo modes.
	::If writing a DataSet Object-enabled preprocessing function, it is expected that the function will calibrate using only included rows but apply and undo the preprocessing to all rows.		::If writing a DataSet Object-enabled preprocessing function, it is expected that the function will calibrate using only included rows but apply and undo the preprocessing to all rows.
	:*<tt>out</tt>: Contents of the preprocessing structure field <tt>out</tt>. Any changes will be stored in the preprocessing structure for use in subsequent <tt>apply</tt> and <tt>undo</tt> commands.		:*<tt>out</tt>: Contents of the preprocessing structure field <tt>out</tt>. Any changes will be stored in the preprocessing structure for use in subsequent <tt>apply</tt> and <tt>undo</tt> commands.
	:*<tt>userdata</tt>: Contents of the preprocessing structure field <tt>userdata</tt>. Any changes will be stored in the preprocessing structure for later retrieval.		:*<tt>userdata</tt>: Contents of the preprocessing structure field <tt>userdata</tt>. Any changes will be stored in the preprocessing structure for later retrieval.
	::'''Read-only variables:''' Do not change the value of these variables.		:'''Read-only variables:''' Do not change the value of these variables.
	:*<tt>include</tt>: Contents of the original DataSet Object's include field.		:*<tt>include</tt>: Contents of the original DataSet Object's include field.
	otherdata: Cell array of additional inputs to preprocess which follow the data. For example, these inputs are used by PLS_Toolbox regression functions to pass the y-block for use in methods which require that information.		:*<tt>otherdata</tt>: Cell array of additional inputs to preprocess which follow the data. For example, these inputs are used by PLS_Toolbox regression functions to pass the y-block for use in methods which require that information.
	:*<tt>originaldata</tt>: Original DataSet Object unmodified by any preprocessing steps. For example, <tt>originaldata</tt> can be used to retrieve axis scale or class information even when <tt>usesdataset</tt> is 0 (zero) and, thus, data is the extracted data.		:*<tt>originaldata</tt>: Original DataSet Object unmodified by any preprocessing steps. For example, <tt>originaldata</tt> can be used to retrieve axis scale or class information even when <tt>usesdataset</tt> is 0 (zero) and, thus, data is the extracted data.

	In order to assure that all samples (rows) in the data have been appropriately preprocessed, an <tt>apply</tt> command is automatically performed following a <tt>calibrate</tt> call. Note that excluded variables are replaced with <tt>NaN</tt>.		:In order to assure that all samples (rows) in the data have been appropriately preprocessed, an <tt>apply</tt> command is automatically performed following a <tt>calibrate</tt> call. Note that excluded variables are replaced with <tt>NaN</tt>.
	:Examples:		:Examples:
	::The following <tt>calibrate</tt> field performs mean-centering on data, returning both the mean-centered data as well as the mean values which are stored in <tt>out{1}</tt>:		::The following <tt>calibrate</tt> field performs mean-centering on data, returning both the mean-centered data as well as the mean values which are stored in <tt>out{1}</tt>:
	::<pre>pp.calibrate = { '[data,out{1}] = mncn(data);' };</pre>		::<pre>pp.calibrate = { '[data,out{1}] = mncn(data);' };</pre>
	The following <tt>apply</tt> and <tt>undo</tt> fields use the <tt>scale</tt> and <tt>rescale</tt> functions to apply and undo the previously determined mean values (stored by the <tt>calibrate</tt> operation in <tt>out{1}</tt>) with new data:		:The following <tt>apply</tt> and <tt>undo</tt> fields use the <tt>scale</tt> and <tt>rescale</tt> functions to apply and undo the previously determined mean values (stored by the <tt>calibrate</tt> operation in <tt>out{1}</tt>) with new data:
	<pre>pp.apply = { 'data = scale(data,out{1});' };		::<pre>pp.apply = { 'data = scale(data,out{1});' };
	pp.undo = { 'data = rescale(data,out{1});' };</pre>		::pp.undo = { 'data = rescale(data,out{1});' };</pre>

	* <tt>out</tt>: Contains non-data outputs from calibrations. This output is usually information derived from the calibration data which is required to apply the preprocessing to new data. This field will be updated following a <tt>calibrate</tt> call, and the entire preprocessing structure will be output. For example, when autoscaling, the mean and standard deviation of all columns are stored as two entries in <tt>out{1}</tt> and <tt>out{2}</tt>. See the related field <tt>caloutputs</tt>. Usually, this field is empty in the default, uncalibrated structure.		* <tt>out</tt>: Contains non-data outputs from calibrations. This output is usually information derived from the calibration data which is required to apply the preprocessing to new data. This field will be updated following a <tt>calibrate</tt> call, and the entire preprocessing structure will be output. For example, when autoscaling, the mean and standard deviation of all columns are stored as two entries in <tt>out{1}</tt> and <tt>out{2}</tt>. See the related field <tt>caloutputs</tt>. Usually, this field is empty in the default, uncalibrated structure.
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	:The design of GUIs for selection of options is beyond the scope of this document and the user is directed to autoset.m and savgolset.m, both of which use GUIs to modify the <tt>userdata</tt> field of a preprocessing structure. Example:		:The design of GUIs for selection of options is beyond the scope of this document and the user is directed to autoset.m and savgolset.m, both of which use GUIs to modify the <tt>userdata</tt> field of a preprocessing structure. Example:
	<pre>pp.settingsgui = 'autoset';</pre>		:<pre>pp.settingsgui = 'autoset';</pre>

	* <tt>settingsonadd</tt>: Contains a Boolean (1=true, 0=false) value indicating if, when the user selects and adds the method in the GUI, the method's <tt>settingsgui</tt> should be automatically invoked. If a method usually requires the user to make some selection of options, <tt>settingsonadd=1</tt> will guarantee that the user has had an opportunity to modify the options or at least choose the default settings.		* <tt>settingsonadd</tt>: Contains a Boolean (1=true, 0=false) value indicating if, when the user selects and adds the method in the GUI, the method's <tt>settingsgui</tt> should be automatically invoked. If a method usually requires the user to make some selection of options, <tt>settingsonadd=1</tt> will guarantee that the user has had an opportunity to modify the options or at least choose the default settings.
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	::1 = function can handle DataSet Objects; preprocess will pass entire DataSet Object. It is the responsibility of the function(s) called by the method to appropriately handle the <tt>include</tt> field.		::1 = function can handle DataSet Objects; preprocess will pass entire DataSet Object. It is the responsibility of the function(s) called by the method to appropriately handle the <tt>include</tt> field.
	::0 = function needs standard MATLAB data type; <tt>preprocess</tt> will extract data from the DataSet Object prior to calling this method and reinsert preprocessed data after the method. Although excluded columns are never extracted and excluded rows are not extracted when performing calibration operations, excluded rows are passed when performing apply and undo operations.		::0 = function needs standard MATLAB data type; <tt>preprocess</tt> will extract data from the DataSet Object prior to calling this method and reinsert preprocessed data after the method. Although excluded columns are never extracted and excluded rows are not extracted when performing calibration operations, excluded rows are passed when performing apply and undo operations.
	::Example:		:Example:

	::<pre>pp.usesdataset = 0;</pre>		:<pre>pp.usesdataset = 0;</pre>

	* <tt>caloutputs</tt>: Contains the number of values expected in field out if a calibrate operation has been performed, for functions which require a <tt>calibrate</tt> operation prior to an apply or undo. For example, in the case of mean centering, the mean values stored in the field out are required to apply or undo the operation. Initially, out is an empty cell ({}) but following the calibration, it becomes a single-item cell (length of one). By examining this length, <tt>preprocess</tt> can determine if a preprocessing structure contains calibration information. The caloutputs field, when greater than zero, indicates to preprocess that it should test the <tt>out</tt> field prior to attempting an apply or undo.		* <tt>caloutputs</tt>: Contains the number of values expected in field out if a calibrate operation has been performed, for functions which require a <tt>calibrate</tt> operation prior to an apply or undo. For example, in the case of mean centering, the mean values stored in the field out are required to apply or undo the operation. Initially, out is an empty cell ({}) but following the calibration, it becomes a single-item cell (length of one). By examining this length, <tt>preprocess</tt> can determine if a preprocessing structure contains calibration information. The caloutputs field, when greater than zero, indicates to preprocess that it should test the <tt>out</tt> field prior to attempting an apply or undo.
	::Example:		:Example:
	:::In the case of mean-centering, the length of out should be 1 (one) after calibration:		:In the case of mean-centering, the length of out should be 1 (one) after calibration:
	:::<pre>pp.caloutputs = 1;</pre>		:<pre>pp.caloutputs = 1;</pre>

	* <tt>keyword</tt>: Contains a string which can be used to retrieve the default preprocessing structure for this method. When retrieving a structure by keyword, preprocess ignores any spaces and is case-insensitive. The keyword (or even the description string) can be used in place of any preprocessing structure in <tt>calibrate</tt> and <tt>default</tt> calls to preprocess:		* <tt>keyword</tt>: Contains a string which can be used to retrieve the default preprocessing structure for this method. When retrieving a structure by keyword, preprocess ignores any spaces and is case-insensitive. The keyword (or even the description string) can be used in place of any preprocessing structure in <tt>calibrate</tt> and <tt>default</tt> calls to preprocess:
	::<pre>pp = preprocess('default','meancenter');</pre>		:<pre>pp = preprocess('default','meancenter');</pre>
	:::Example:		:Example:
	:::<pre>pp.keyword = 'Mean Center';</pre>		:<pre>pp.keyword = 'Mean Center';</pre>

			* <tt>category</tt>: Contains a string which associates this preprocessing method with other similar methods in the list of preprocessing methods. It is used only to make it easier on the user to locate the method. The string can be one of the standard categories: "Filtering", "Normalization", "Scaling and Centering", "Transformations", "Other", or any other string defining a user-defined category.
			:Examples:
			:<pre>pp.category = 'Filtering';</pre>
			:<pre>pp.category = 'My Methods';</pre>

	* <tt>userdata</tt>: A user-defined field. This field is often used to hold options for the particular method. This field can also be updated following a calibrate operation.		* <tt>userdata</tt>: A user-defined field. This field is often used to hold options for the particular method. This field can also be updated following a calibrate operation.
	::Example:		:Example:
	:::In savgol several variables are defined with various method options, then they are assembled into userdata:		:In savgol several variables are defined with various method options, then they are assembled into userdata:
	:::<pre>pp.userdata = [windowsize order derivative];</pre>		:<pre>pp.userdata = [windowsize order derivative];</pre>

	==Examples==		==Examples==