paraview.simple.DescriptiveStatistics¶

paraview.simple.DescriptiveStatistics(*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. This filter computes the min, max, mean, raw moments M2 through M4, standard deviation, skewness, and kurtosis for each array you select. The model is simply a univariate Gaussian distribution with the mean and standard deviation provided. Data is assessed using this model by detrending the data (i.e., subtracting the mean) and then dividing by the standard deviation. Thus the assessment is an array whose entries are the number of standard deviations from the mean that each input point lies.

Data Descriptors¶

AttributeMode
Specify which type of field data the arrays will be drawn from.

Deviationsshouldbe
Should the assessed values be signed deviations or unsigned?

Input
The input to the filter. Arrays from this dataset will be used for computing statistics and/or assessed by a statistical model.

ModelInput
A previously-calculated model with which to assess a separate dataset. This input is optional.

Task
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 entire input dataset;</li> <li> “Model a subset of the data,” creates an output table (or tables) summarizing a randomly-chosen subset 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 Training fraction setting will be ignored for tasks 1 and 3.

TrainingFraction
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.

VariablesofInterest
Choose arrays whose entries will be used to form observations for statistical analysis.

Data Descriptors inherited from Proxy¶

__dict__
dictionary for instance variables (if defined)

__weakref__
list of weak references to the object (if defined)

Methods¶

Initialize = aInitialize(self, connection=None, update=True)

Methods inherited from SourceProxy¶

FileNameChanged(self)
Called when the filename of a source proxy is changed.

GetCellDataInformation(self)
Returns the associated cell data information.

GetDataInformation(self, idx=None)
This method returns a DataInformation wrapper around a vtkPVDataInformation

GetFieldDataInformation(self)
Returns the associated cell data information.

GetPointDataInformation(self)
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.

UpdatePipelineInformation(self)
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.

ListProperties(self)
Returns a list of all property names on this proxy.

SetPropertyWithName(self, pname, arg)
Generic method for setting the value of a property.

__del__(self)
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

__hash__(self)
Return hash(self).

__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.

__iter__(self)
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