ParaView/ServerManager XMLs — French @ Weblate: Python Calculator

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Key ServerManagerXML

English Real source: VTKExtensions/FiltersPython/Resources/python_filters.xml - VTKExtensions/FiltersPython/Resources/python_filters.xml

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Key	English	French
ServerManagerXML	Specify the relative tolerance that will cause early termination.
ServerManagerXML	Multicorrelative Statistics	Statistiques multicorrélées
ServerManagerXML	This filter either computes a statistical model of a dataset or takes such a model as its second input. Then, the model (however it is obtained) may optionally be used to assess the input dataset.<p> This filter computes the covariance matrix for all the arrays you select plus the mean of each array. The model is thus a multivariate Gaussian distribution with the mean vector and variances provided. Data is assessed using this model by computing the Mahalanobis distance for each input point. This distance will always be positive.<p> The learned model output format is rather dense and can be confusing, so it is discussed here. The first filter output is a multiblock dataset consisting of 2 tables: <ol> <li> Raw covariance data. <li> Covariance matrix and its Cholesky decomposition. </ol> The raw covariance table has 3 meaningful columns: 2 titled \"Column1\" and \"Column2\" whose entries generally refer to the N arrays you selected when preparing the filter and 1 column titled \"Entries\" that contains numeric values. The first row will always contain the number of observations in the statistical analysis. The next N rows contain the mean for each of the N arrays you selected. The remaining rows contain covariances of pairs of arrays.<p> The second table (covariance matrix and Cholesky decomposition) contains information derived from the raw covariance data of the first table. The first N rows of the first column contain the name of one array you selected for analysis. These rows are followed by a single entry labeled \"Cholesky\" for a total of N+1 rows. The second column, Mean contains the mean of each variable in the first N entries and the number of observations processed in the final (N+1) row.<p> The remaining columns (there are N, one for each array) contain 2 matrices in triangular format. The upper right triangle contains the covariance matrix (which is symmetric, so its lower triangle may be inferred). The lower left triangle contains the Cholesky decomposition of the covariance matrix (which is triangular, so its upper triangle is zero). Because the diagonal must be stored for both matrices, an additional row is required â€” hence the N+1 rows and the final entry of the column named \"Column\".
ServerManagerXML	Principal Component Analysis
ServerManagerXML	This filter either computes a statistical model of a dataset or takes such a model as its second input. Then, the model (however it is obtained) may optionally be used to assess the input dataset. <p> This filter performs additional analysis above and beyond the multicorrelative filter. It computes the eigenvalues and eigenvectors of the covariance matrix from the multicorrelative filter. Data is then assessed by projecting the original tuples into a possibly lower-dimensional space. <p> Since the PCA filter uses the multicorrelative filter's analysis, it shares the same raw covariance table specified in the multicorrelative documentation. The second table in the multiblock dataset comprising the model output is an expanded version of the multicorrelative version. <p> As with the multicorrelative filter, the second model table contains the mean values, the upper-triangular portion of the symmetric covariance matrix, and the non-zero lower-triangular portion of the Cholesky decomposition of the covariance matrix. Below these entries are the eigenvalues of the covariance matrix (in the column labeled \"Mean\") and the eigenvectors (as row vectors) in an additional NxN matrix.
ServerManagerXML	Normalization Scheme
ServerManagerXML	Before the eigenvector decomposition of the covariance matrix takes place, you may normalize each (i,j) entry by sqrt( cov(i,i) * cov(j,j) ). This implies that the variance of each variable of interest should be of equal importance.
ServerManagerXML	Basis Scheme
ServerManagerXML	When reporting assessments, should the full eigenvector decomposition be used to project the original vector into the new space (Full basis), or should a fixed subset of the decomposition be used (Fixed-size basis), or should the projection be clipped to preserve at least some fixed \"energy\" (Fixed-energy basis)?<p> As an example, suppose the variables of interest were {A,B,C,D,E} and that the eigenvalues of the covariance matrix for these were {5,2,1.5,1,.5}. If the \"Full basis\" scheme is used, then all 5 components of the eigenvectors will be used to project each {A,B,C,D,E}-tuple in the original data into a new 5-components space.<p> If the \"Fixed-size\" scheme is used and the \"Basis Size\" property is set to 4, then only the first 4 eigenvector components will be used to project each {A,B,C,D,E}-tuple into the new space and that space will be of dimension 4, not 5.<p> If the \"Fixed-energy basis\" scheme is used and the \"Basis Energy\" property is set to 0.8, then only the first 3 eigenvector components will be used to project each {A,B,C,D,E}-tuple into the new space, which will be of dimension 3. The number 3 is chosen because 3 is the lowest N for which the sum of the first N eigenvalues divided by the sum of all eigenvalues is larger than the specified \"Basis Energy\" (i.e., (5+2+1.5)/10 = 0.85 > 0.8).
ServerManagerXML	Basis Size
ServerManagerXML	The maximum number of eigenvector components to use when projecting into the new space.
ServerManagerXML	Basis Energy
ServerManagerXML	The minimum energy to use when determining the dimensionality of the new space into which the assessment will project tuples.
ServerManagerXML	Robust PCA
ServerManagerXML	Compute robust PCA with medians instead of means.
ServerManagerXML	Python Calculator
ServerManagerXML	This filter uses Python to calculate an expression. It depends heavily on the numpy and paraview.vtk modules. To use the parallel functions, mpi4py is also necessary. The expression is evaluated and the resulting scalar value or numpy array is added to the output as an array. See numpy integration and python calculator documentation for the list of available functions. This filter tries to make it easy for the user to write expressions by defining certain variables. The filter tries to assign each array to a variable of the same name. If the name of the array is not a valid Python variable, it may be accessed through a variable with a sanitized version of that array name consisting of only '_' and the alphanumeric characters in the name joined together with no spaces. Point coordinates from datasets that explicitly define them can be accessed using the variable named 'points'.
ServerManagerXML	This filter evaluates a Python expression
ServerManagerXML	Evaluates a Python expression
ServerManagerXML	Use Multiline Expression
ServerManagerXML	If this property is set to true, the expression is a multiline script that returns the output array.
ServerManagerXML	The Python expression evaluated during execution. Set of potentially available variables [inputs, points, t_value, t_index, FieldData, PointData, CellData] (i.e.: \"Momentum: (%f, %f, %f)\" % (XMOM[t_index,0], YMOM[t_index,0], ZMOM[t_index,0]) ). Additional variables, using the fmt library's syntax, are {timevalue}, and {timeindex}. These variables are always available, and their default values are 0, and 0 respectively.
ServerManagerXML	Multiline Expression
ServerManagerXML	The multiline Python expression executed, that should return the output array using the Python \"return\" keyword. The variables that can be used here are the exact same as the ones available in the single-line expression editor.
ServerManagerXML	Array Association
ServerManagerXML	The name of the output array.
ServerManagerXML	Copy Arrays
ServerManagerXML	If this property is set to true, all the cell and point arrays from first input are copied to the output.
ServerManagerXML	Result Array Type
ServerManagerXML	This property determines what array type to output. The default is a vtkDoubleArray.
ServerManagerXML	Annotate Attribute Data

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a year ago

mathieu.westphal

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Key

ServerManagerXML

Source string description

Real source: VTKExtensions/FiltersPython/Resources/python_filters.xml - VTKExtensions/FiltersPython/Resources/python_filters.xml

Source string location

../build/translationSourcesparaview_server_manager.h:25696

String age

a year ago

Last updated

a week ago

Source string age

a year ago

Translation file

fr_FR/ServerManager-XMLs.ts, string 3974