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pa-data-conditioning [2026/01/30 21:14] – reid_go-ci.compa-data-conditioning [2026/01/30 21:21] (current) – reid_go-ci.com
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 Statistics supports a variety of functions used in data interpretation.  Including several common functions i.e. Min, Max, Mean.  It also supports more specialized operations: Statistics supports a variety of functions used in data interpretation.  Including several common functions i.e. Min, Max, Mean.  It also supports more specialized operations:
 Peak to Peak operator saves the greatest displacement between signal minimum and maximum of oscillation during time block.   Peak to Peak operator saves the greatest displacement between signal minimum and maximum of oscillation during time block.  
-RMS calculates the effective signal strength over a period.   +RMS calculates the effective signal strength over a period.  {{::rms_eqn.png?200|}}
- +
-{{::rms_eqn.png?600|}}+
  
 ===Integration/Differentiation=== ===Integration/Differentiation===
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 Have a set time duration of response.  FIR filters do not have a feedback loop and feature a constant group delay.  This means that an input signal will have a uniformly delayed output and will have no distortion.  FIR filters are defined by:  Have a set time duration of response.  FIR filters do not have a feedback loop and feature a constant group delay.  This means that an input signal will have a uniformly delayed output and will have no distortion.  FIR filters are defined by: 
  
-{{::fir_filter_eqn.png?600|}}+{{::fir_filter_eqn.png?500|}}
  
 FIR Filter-Window is the simplest method of constructing a FIR filter.  Filters are created by scaling a sinc function.  It begins with a perfect passband filter and is constructed with filter coefficients to generate a desired frequency response. FIR Filter-Window is the simplest method of constructing a FIR filter.  Filters are created by scaling a sinc function.  It begins with a perfect passband filter and is constructed with filter coefficients to generate a desired frequency response.
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 Infinite Impulse Response (IIR) filters are defined to last forever and decay slowly.  Defined by the equation: Infinite Impulse Response (IIR) filters are defined to last forever and decay slowly.  Defined by the equation:
    
-{{:iir_filter_eqn.png?600|}}+{{:iir_filter_eqn.png?800|}}
  
 There is no set method of creation for IIR filters.  In PA the user defines the decimation filter, filter type, filter order, analog prototype, and cutoff frequencies.   There is no set method of creation for IIR filters.  In PA the user defines the decimation filter, filter type, filter order, analog prototype, and cutoff frequencies.  
 Filter_RMS closely resembles the output of 1/N octave filters but with greater flexibility.  Users can design any filter and examine the RMS.  Functionally the same as computing the RMS after applying an RMS filter. Filter_RMS closely resembles the output of 1/N octave filters but with greater flexibility.  Users can design any filter and examine the RMS.  Functionally the same as computing the RMS after applying an RMS filter.
  
 +===Python Script===
 +
 +{{:dc_python.png?600|}}
 +
 +User can import a python script with a single or two inputs.
  
 ====Simple Data Conditioning Demonstration==== ====Simple Data Conditioning Demonstration====
 +
 +Modules can be easily dragged into place and connected to the time stream channel output by click and drag.  Module parameters are opened and modified by right clicking on the parameter and selecting Edit Analysis Parameters 
  
 {{::2026-01-30_11-27-36.gif}} {{::2026-01-30_11-27-36.gif}}