https://sourceforge.net/p/freemuse/wiki/Basic%2520models/Recent changes to Basic modelsenSat, 04 Jun 2011 07:58:36 -0000https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v7 +++ v8 @@ -120,7 +120,7 @@ </calculation> </simulation> ~~~~~~ -First we say, we want an instance and override the default parameters of the file. Parameter __#1__ will now be set to a formula, parameter __#2__ to a constant of 2. The variable __time__ used in the first parameter is defined in the [Process section](process section), where it uses the global variable __start__ as another varying factor. In the end we have an influence gaussian distributed with a drifting mean with values 0, 1, 2, . . . , 10, 11, 12 and a standard deviation of 2. +First we say, we want an instance and override the default parameters of the file. Parameter __#1__ will now be set to a formula, parameter __#2__ to a constant of 2. The variable __time__ used in the first parameter is defined in the [process section](Process section), where it uses the global variable __start__ as another varying factor. In the end we have an influence gaussian distributed with a drifting mean with values 0, 1, 2, . . . , 10, 11, 12 and a standard deviation of 2. Parametrization using variables --- </pre>Marco WolfSat, 04 Jun 2011 07:58:36 -0000https://sourceforge.net18d1870e33ee2ee82fba87bd9fb15b55e06cf0e2https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v6 +++ v7 @@ -77,6 +77,7 @@ Basic models are an abstract form of defining the dependencies of parts of a measurement with influences and distributions. [Instances of basic models](Initializing section) may need different value settings for the parameters of the distributions and one may not like to change basic models directly, but set parameters from outside, say from the part where one instanciates the basic models. __MUSE__ supports two different types of parametrization: + 1. ___Parameters___: A parameter replaces the setting of a value in a distribution of an instance of a basic model with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. * ___Variables___: A variable represents exactly one value at a time. As parameters can also push formula definitions to the library files, variables just replace the variable name by their current value. As the distinction of these two concepts is not that easy to understand, it should be clarified using an example. </pre>Marco WolfSat, 04 Jun 2011 07:58:08 -0000https://sourceforge.net78be66bc30826e67644176e51030557db275e289https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v5 +++ v6 @@ -21,7 +21,7 @@ Body of a model --- -In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [probability distributions](Distributions), if neccessary. The distribution of each influence can be [Logging influences](logged). +In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [probability distributions](Distributions), if neccessary. The distribution of each influence can be [logged](Logging influences). Example 1: Simple influence structure --- </pre>Marco WolfSat, 04 Jun 2011 07:56:45 -0000https://sourceforge.net48779a7367941f6072ea6e1f4d7c0b0ea7625bcahttps://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v4 +++ v5 @@ -74,11 +74,10 @@ Parametrizing basic models --- -Basic models are an abstract form of defining the dependencies of parts of a measurement with influences and distributions. [Initialization section](Instances of basic models) may need different value settings for the parameters of the distributions and one may not like to change basic models directly, but set parameters from outside, say from the part where one instanciates the basic models. - +Basic models are an abstract form of defining the dependencies of parts of a measurement with influences and distributions. [Instances of basic models](Initializing section) may need different value settings for the parameters of the distributions and one may not like to change basic models directly, but set parameters from outside, say from the part where one instanciates the basic models. + __MUSE__ supports two different types of parametrization: - -1. ___Parameters___: A parameter replaces the setting of a value in a distribution of an instance of a [Basic models](basic model) with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. +1. ___Parameters___: A parameter replaces the setting of a value in a distribution of an instance of a basic model with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. * ___Variables___: A variable represents exactly one value at a time. As parameters can also push formula definitions to the library files, variables just replace the variable name by their current value. As the distinction of these two concepts is not that easy to understand, it should be clarified using an example. </pre>Marco WolfSat, 04 Jun 2011 07:56:03 -0000https://sourceforge.net1850e8805613539c187b6e35b36691393e11b264https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v3 +++ v4 @@ -21,7 +21,7 @@ Body of a model --- -In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [Distributions](probability distributions), if neccessary. The distribution of each influence can be [Logging influences](logged). +In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [probability distributions](Distributions), if neccessary. The distribution of each influence can be [Logging influences](logged). Example 1: Simple influence structure --- </pre>Marco WolfThu, 02 Jun 2011 12:31:26 -0000https://sourceforge.net48d423ccbdd4bcc487de3942cf130e89c24f6a31https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v2 +++ v3 @@ -39,7 +39,7 @@ </influence> </model> ~~~~~~ -To see what kind of distributions are support please refer to the chapter about [Distributions](supported distributions). +To see what kind of distributions are support please refer to the chapter about [supported distributions](Distributions). Example 2: Subinfluences --- </pre>Marco WolfThu, 02 Jun 2011 12:29:32 -0000https://sourceforge.net728a88cbe751cad16a236c6ccbc5bee9b927a209https://sourceforge.net/p/freemuse/wiki/Basic%2520models/<pre>--- v1 +++ v2 @@ -15,16 +15,16 @@ ~~~~~~ The first line is used to identify the file as correct xml file, the second line tells the parser how to check the validity of the syntax in the model. Users do not have to understand these lines in detail. The interesting part follows after these two standard lines. -The ''model'' tag opens a basic model, in the example named ''length'' and the tag ''targetname'' tells the parser which influence is the main influence. - +The __model__ tag opens a basic model, in the example named __length__ and the tag __targetname__ tells the parser which influence is the main influence. + In the following subsections we omitt the xml opening lines and concentrate more on the details of the sections of interest. Body of a model --- -In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [[Distributions|probability distributions]], if neccessary. The distribution of each influence can be [[Logging_influences|logged]]. +In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [Distributions](probability distributions), if neccessary. The distribution of each influence can be [Logging influences](logged). Example 1: Simple influence structure --- The following example is a really simple one. The model only consists of the main influence. It is gaussian distributed with parameters 5.0 for mu and 2.0 for sigma. ~~~~~~ @@ -32,37 +32,37 @@ <influence comment="result" name="l"> <distribution> <gauss> <mu>5.0</mu> <sigma>2.0</sigma> </gauss> </distribution> </influence> </model> ~~~~~~ -To see what kind of distributions are support please refer to the chapter about [[Distributions|supported distributions]]. +To see what kind of distributions are support please refer to the chapter about [Distributions](supported distributions). Example 2: Subinfluences --- -A more advanced example uses two subinfluences ''g1'' and ''g2'', where the main influence adds those two subinfluences using the ''function'' tag. +A more advanced example uses two subinfluences __g1__ and __g2__, where the main influence adds those two subinfluences using the __function__ tag. ~~~~~~ <model name="length" targetname="l"> <influence comment="result" name="l"> <formula>g1 + g2</formula> <influences> <influence name="Gauss 1" id="g1"> <distribution> <gauss> <mu>5.0</mu> <sigma>2.0</sigma> </gauss> </distribution> </influence> <influence comment="Gauss 2" name="g2"> <distribution> <gauss> <mu>10.0</mu> <sigma>1.0</sigma> </gauss> </distribution> @@ -72,16 +72,16 @@ </model> ~~~~~~ Parametrizing basic models --- Basic models are an abstract form of defining the dependencies of parts of a measurement with influences and distributions. [Initialization section](Instances of basic models) may need different value settings for the parameters of the distributions and one may not like to change basic models directly, but set parameters from outside, say from the part where one instanciates the basic models. -''MUSE'' supports two different types of parametrization: - -1. '''Parameters''': A parameter replaces the setting of a value in a distribution of an instance of a [[Basic_models|basic model]] with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. -* '''Variables''': A variable represents exactly one value at a time. As parameters can also push formula definitions to the library files, variables just replace the variable name by their current value. +__MUSE__ supports two different types of parametrization: + +1. ___Parameters___: A parameter replaces the setting of a value in a distribution of an instance of a [Basic models](basic model) with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. +* ___Variables___: A variable represents exactly one value at a time. As parameters can also push formula definitions to the library files, variables just replace the variable name by their current value. As the distinction of these two concepts is not that easy to understand, it should be clarified using an example. Parametrization using parameters --- ~~~~~~ @@ -89,30 +89,30 @@ <influence comment="influence" name="v"> <distribution> <gauss> <mu parameter="#1"/> <sigma parameter="#2"/> </gauss> </distribution> </influence> </model> ~~~~~~ -So in the model definition we define one influence named ''influence'' which is gaussian distributed. The mean and standard deviation are parametrized. That means the value (or formula!) of parameter ''#1'' or respectively ''#2'' will be used for the parameters of the distribution. Let us continue with the simulation file: +So in the model definition we define one influence named __influence__ which is gaussian distributed. The mean and standard deviation are parametrized. That means the value (or formula!) of parameter __#1__ or respectively __#2__ will be used for the parameters of the distribution. Let us continue with the simulation file: ~~~~~~ <simulation> <instances> <instance model="influence" name="v1"> <parameters> <parameter id = "#1">time 0.1*</parameter> <parameter id = "#2">2</parameter> </parameters> </instance> </instances> <processes> <process name="v1"> <variable name="time"> start 10+ </variable> <formula>time</formula> </process> </processes> <calculation> <variation name="start" from="0" to="120" step="10"/> @@ -120,10 +120,10 @@ </calculation> </simulation> ~~~~~~ -First we say, we want an instance and override the default parameters of the file. Parameter ''#1'' will now be set to a formula, parameter ''#2'' to a constant of 2. The variable ''time'' used in the first parameter is defined in the [[Process_section|process section]], where it uses the global variable ''start'' as another varying factor. In the end we have an influence gaussian distributed with a drifting mean with values 0, 1, 2, . . . , 10, 11, 12 and a standard deviation of 2. +First we say, we want an instance and override the default parameters of the file. Parameter __#1__ will now be set to a formula, parameter __#2__ to a constant of 2. The variable __time__ used in the first parameter is defined in the [Process section](process section), where it uses the global variable __start__ as another varying factor. In the end we have an influence gaussian distributed with a drifting mean with values 0, 1, 2, . . . , 10, 11, 12 and a standard deviation of 2. Parametrization using variables --- The definiton of the model file using just variables, not parameters, would look like this. ~~~~~~ @@ -131,62 +131,62 @@ <influence comment="influence" name="v"> <distribution> <gauss> <mu parameter="#1">mymu</mu> <sigma parameter="#2">mysigma</sigma> </gauss> </distribution> </influence> </model> ~~~~~~ -In this file we use to variables ''mymu'' and ''mysigma'' that are not defined in the model file. Therefore the system expects their definitions in the simulation file. +In this file we use to variables __mymu__ and __mysigma__ that are not defined in the model file. Therefore the system expects their definitions in the simulation file. ~~~~~~ <simulation> <instances> <instance model="influence" name="v1"/> </instances> <process> <step name="v1"> <variable name="mymu"> start 10+ </variable> </step> </process> <calculation> <variation name="start" from="0" to="120" step="10"/> <variable name="mysigma"> 1.5 </variable> <measurand> v1 </measurand> </calculation> </simulation> ~~~~~~ -The values of ''mymu'' and ''mysigma'' will be used as defined in the simulation file. If you do not define missing variables of the models in the simulation file, the system will respond with an error message. - -'''''Hint:''''' We recommend to use parametrization, as you can define default values and for better readability of your models. A parameter is explicitly declared once in the model file and then in the simulation file, if you do not use its default value. - -'''''Hint:''''' If you take a model using variables and have not defined the named variables properly in the calcualtion section, the parser will return an error message everytime the formula is parsed. +The values of __mymu__ and __mysigma__ will be used as defined in the simulation file. If you do not define missing variables of the models in the simulation file, the system will respond with an error message. + +_____Hint:_____ We recommend to use parametrization, as you can define default values and for better readability of your models. A parameter is explicitly declared once in the model file and then in the simulation file, if you do not use its default value. + +_____Hint:_____ If you take a model using variables and have not defined the named variables properly in the calcualtion section, the parser will return an error message everytime the formula is parsed. Recursive definition of basic models --- It is possible to use basic models as influences in basic models. Like this you can define a nice hierarchy of models. See section [Recursive definition of basicmodels] for more information. Static influences --- -Sometimes it seems reasonable to define not a complete [Initialization section](instance) as static, but only parts of it. That means, if you use an instance of a basic model for example the temperature should be kept to the same value for all calculations using this instance. You can do that by defining an influence in mode ''static''. +Sometimes it seems reasonable to define not a complete [Initialization section](instance) as static, but only parts of it. That means, if you use an instance of a basic model for example the temperature should be kept to the same value for all calculations using this instance. You can do that by defining an influence in mode __static__. ~~~~~~ <model ...> <influence comment="concentration" name="c"> ... <influences> <influence comment="temperature" name="t" mode="static"> ... </influence> ... </influences> </influence> </model> ~~~~~~ -'''''Attention:''''' If you define an influence as ''static'' in a basic model, all subsequent influences to that one will also be defined as ''static''. - -'''''Attention:''''' The keyword ''static'' always only applies to the current instance. If you use for example two instances of a volume device with a basic model that uses static influences, they are only static for each of the volume devices. +_____Attention:_____ If you define an influence as __static__ in a basic model, all subsequent influences to that one will also be defined as __static__. + +_____Attention:_____ The keyword __static__ always only applies to the current instance. If you use for example two instances of a volume device with a basic model that uses static influences, they are only static for each of the volume devices. Missing topic === </pre>Marco WolfThu, 02 Jun 2011 12:01:26 -0000https://sourceforge.netf5d7c0eccb8466fa380f80f44164775330d1c078https://sourceforge.net/p/freemuse/wiki/Basic%2520models/Basic Models === The ideas behind basic models is that logical elements of a measurement, e.g. devices, substances, can be encapsulated in abstract moduls and be instanciated for the definition of a concrete measurement scenario. Each basic model is defined in one xml file and can be parametrised so that if used parameters can be set without touching the file itself. Format of basic model files === Frame of a model --- The frame of a basic model consist of the following few lines. ~~~~~~ <model name="length" targetname="l"> ... </model> ~~~~~~ The first line is used to identify the file as correct xml file, the second line tells the parser how to check the validity of the syntax in the model. Users do not have to understand these lines in detail. The interesting part follows after these two standard lines. The ''model'' tag opens a basic model, in the example named ''length'' and the tag ''targetname'' tells the parser which influence is the main influence. In the following subsections we omitt the xml opening lines and concentrate more on the details of the sections of interest. Body of a model --- In the body of a model you define your influences to the measurement by defining recursively subinfluences and quantifying them by setting parameters of their [[Distributions|probability distributions]], if neccessary. The distribution of each influence can be [[Logging_influences|logged]]. Example 1: Simple influence structure --- The following example is a really simple one. The model only consists of the main influence. It is gaussian distributed with parameters 5.0 for mu and 2.0 for sigma. ~~~~~~ <model name="length" targetname="l"> <influence comment="result" name="l"> <distribution> <gauss> <mu>5.0</mu> <sigma>2.0</sigma> </gauss> </distribution> </influence> </model> ~~~~~~ To see what kind of distributions are support please refer to the chapter about [[Distributions|supported distributions]]. Example 2: Subinfluences --- A more advanced example uses two subinfluences ''g1'' and ''g2'', where the main influence adds those two subinfluences using the ''function'' tag. ~~~~~~ <model name="length" targetname="l"> <influence comment="result" name="l"> <formula>g1 + g2</formula> <influences> <influence name="Gauss 1" id="g1"> <distribution> <gauss> <mu>5.0</mu> <sigma>2.0</sigma> </gauss> </distribution> </influence> <influence comment="Gauss 2" name="g2"> <distribution> <gauss> <mu>10.0</mu> <sigma>1.0</sigma> </gauss> </distribution> </influence> </influences> </influence> </model> ~~~~~~ Parametrizing basic models --- Basic models are an abstract form of defining the dependencies of parts of a measurement with influences and distributions. [Initialization section](Instances of basic models) may need different value settings for the parameters of the distributions and one may not like to change basic models directly, but set parameters from outside, say from the part where one instanciates the basic models. ''MUSE'' supports two different types of parametrization: 1. '''Parameters''': A parameter replaces the setting of a value in a distribution of an instance of a [[Basic_models|basic model]] with its default value or a new value set in the main program. It allows to override default settings in the library files that are optional. * '''Variables''': A variable represents exactly one value at a time. As parameters can also push formula definitions to the library files, variables just replace the variable name by their current value. As the distinction of these two concepts is not that easy to understand, it should be clarified using an example. Parametrization using parameters --- ~~~~~~ <model ...> <influence comment="influence" name="v"> <distribution> <gauss> <mu parameter="#1"/> <sigma parameter="#2"/> </gauss> </distribution> </influence> </model> ~~~~~~ So in the model definition we define one influence named ''influence'' which is gaussian distributed. The mean and standard deviation are parametrized. That means the value (or formula!) of parameter ''#1'' or respectively ''#2'' will be used for the parameters of the distribution. Let us continue with the simulation file: ~~~~~~ <simulation> <instances> <instance model="influence" name="v1"> <parameters> <parameter id = "#1">time 0.1*</parameter> <parameter id = "#2">2</parameter> </parameters> </instance> </instances> <processes> <process name="v1"> <variable name="time"> start 10+ </variable> <formula>time</formula> </process> </processes> <calculation> <variation name="start" from="0" to="120" step="10"/> <measurand> v1 </measurand> </calculation> </simulation> ~~~~~~ First we say, we want an instance and override the default parameters of the file. Parameter ''#1'' will now be set to a formula, parameter ''#2'' to a constant of 2. The variable ''time'' used in the first parameter is defined in the [[Process_section|process section]], where it uses the global variable ''start'' as another varying factor. In the end we have an influence gaussian distributed with a drifting mean with values 0, 1, 2, . . . , 10, 11, 12 and a standard deviation of 2. Parametrization using variables --- The definiton of the model file using just variables, not parameters, would look like this. ~~~~~~ <model ...> <influence comment="influence" name="v"> <distribution> <gauss> <mu parameter="#1">mymu</mu> <sigma parameter="#2">mysigma</sigma> </gauss> </distribution> </influence> </model> ~~~~~~ In this file we use to variables ''mymu'' and ''mysigma'' that are not defined in the model file. Therefore the system expects their definitions in the simulation file. ~~~~~~ <simulation> <instances> <instance model="influence" name="v1"/> </instances> <process> <step name="v1"> <variable name="mymu"> start 10+ </variable> </step> </process> <calculation> <variation name="start" from="0" to="120" step="10"/> <variable name="mysigma"> 1.5 </variable> <measurand> v1 </measurand> </calculation> </simulation> ~~~~~~ The values of ''mymu'' and ''mysigma'' will be used as defined in the simulation file. If you do not define missing variables of the models in the simulation file, the system will respond with an error message. '''''Hint:''''' We recommend to use parametrization, as you can define default values and for better readability of your models. A parameter is explicitly declared once in the model file and then in the simulation file, if you do not use its default value. '''''Hint:''''' If you take a model using variables and have not defined the named variables properly in the calcualtion section, the parser will return an error message everytime the formula is parsed. Recursive definition of basic models --- It is possible to use basic models as influences in basic models. Like this you can define a nice hierarchy of models. See section [Recursive definition of basicmodels] for more information. Static influences --- Sometimes it seems reasonable to define not a complete [Initialization section](instance) as static, but only parts of it. That means, if you use an instance of a basic model for example the temperature should be kept to the same value for all calculations using this instance. You can do that by defining an influence in mode ''static''. ~~~~~~ <model ...> <influence comment="concentration" name="c"> ... <influences> <influence comment="temperature" name="t" mode="static"> ... </influence> ... </influences> </influence> </model> ~~~~~~ '''''Attention:''''' If you define an influence as ''static'' in a basic model, all subsequent influences to that one will also be defined as ''static''. '''''Attention:''''' The keyword ''static'' always only applies to the current instance. If you use for example two instances of a volume device with a basic model that uses static influences, they are only static for each of the volume devices. Missing topic === * [Correlated_input_quantities]. Marco WolfThu, 02 Jun 2011 11:56:52 -0000https://sourceforge.net85b926b504fed1715576b94e65bc7eb5fab0a038