- paraview.simple.MulticorrelativeStatistics(*input, **params)¶
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”.
Specify which type of field data the arrays will be drawn from.
The input to the filter. Arrays from this dataset will be used for computing statistics and/or assessed by a statistical model.
A previously-calculated model with which to assess a separate dataset. This input is optional.
Specify the task to be performed: modeling and/or assessment. <ol> <li> “Detailed model of input data,” creates a set of output tables containing a calculated statistical model of the <b>entire</b> input dataset;</li> <li> “Model a subset of the data,” creates an output table (or tables) summarizing a <b>randomly-chosen subset</b> of the input dataset;</li> <li> “Assess the data with a model,” adds attributes to the first input dataset using a model provided on the second input port; and</li> <li> “Model and assess the same data,” is really just operations 2 and 3 above applied to the same input dataset. The model is first trained using a fraction of the input data and then the entire dataset is assessed using that model.</li> </ol> When the task includes creating a model (i.e., tasks 2, and 4), you may adjust the fraction of the input dataset used for training. You should avoid using a large fraction of the input data for training as you will then not be able to detect overfitting. The <i>Training fraction</i> setting will be ignored for tasks 1 and 3.
Specify the fraction of values from the input dataset to be used for model fitting. The exact set of values is chosen at random from the dataset.
Choose arrays whose entries will be used to form observations for statistical analysis.
Data Descriptors inherited from Proxy¶
dictionary for instance variables (if defined)
list of weak references to the object (if defined)
Initialize = aInitialize(self, connection=None, update=True)
Methods inherited from SourceProxy¶
Called when the filename of a source proxy is changed.
Returns the associated cell data information.
- GetDataInformation(self, idx=None)
This method returns a DataInformation wrapper around a vtkPVDataInformation
Returns the associated cell data information.
Returns the associated point data information.
- UpdatePipeline(self, time=None)
This method updates the server-side VTK pipeline and the associated data information. Make sure to update a source to validate the output meta-data.
This method updates the meta-data of the server-side VTK pipeline and the associated information properties
- __getitem__(self, idx)
Given a slice, int or string, returns the corresponding output port
Methods inherited from Proxy¶
- GetProperty(self, name)
Given a property name, returns the property object.
- GetPropertyValue(self, name)
Returns a scalar for properties with 1 elements, the property itself for vectors.
- InitializeFromProxy(self, aProxy, update=True)
Constructor. Assigns proxy to self.SMProxy, updates the server object as well as register the proxy in _pyproxies dictionary.
Returns a list of all property names on this proxy.
- SetPropertyWithName(self, pname, arg)
Generic method for setting the value of a property.
Destructor. Cleans up all observers as well as remove the proxy from the _pyproxies dictionary
- __eq__(self, other)
Returns true if the underlying SMProxies are the same.
- __getattr__(self, name)
With the exception of a few overloaded methods, returns the SMProxy method
- __init__(self, **args)
Default constructor. It can be used to initialize properties by passing keyword arguments where the key is the name of the property. In addition registrationGroup and registrationName (optional) can be specified (as keyword arguments) to automatically register the proxy with the proxy manager.
Creates an iterator for the properties.
- __ne__(self, other)
Returns false if the underlying SMProxies are the same.
- __setattr__(self, name, value)
Implement setattr(self, name, value).
add_attribute(self, name, value)
For the full list of servermanager proxies, please refer to Available readers, sources, writers, filters and animation cues