# areaDetector Plugin NDPluginProcess

## October 1, 2012

## Mark Rivers

## University of Chicago

## Contents

- Overview
- Configuration
- Screen shots

## Overview

NDPluginProcess performs arithmetic processing on NDArray data. It performs the following operations in the order listed. Each of these operations can be individually enabled and disabled.

1. Subtracts a background array which has been previously acquired.
2. Divides by a flat field array which has been previously acquired, and then multiplies by a flat field scale factor.
3. Multiplies by a scale factor and adds an offset.
4. Clips to a minimum specified value.
5. Clips to a minimum specified value.
6. Applies a recursive digital filter.
7. Converts to the specified output data type.
8. Exports the processed data as a new NDArray object.

If any of the above operations is enabled, then the array is first converted to NDFloat64 data type, i.e. double-precision float. The operations are all performed in double-precision, and then the array is converted to the specified output data type.

NDPluginProcess is both a recipient of callbacks and a source of NDArray callbacks. This means that other plugins, such the NDPluginStdArrays, NDPluginStats, and NDPluginFile plugins can be connected to an NDPluginProcess plugin, in which case they will use the processed data.

NDPluginProcess is fully N-dimensional. It can be used for 2-D images, 3-D (color) images, or any type of N-dimensional data.

NDPluginProcess inherits from NDPluginDriver. The NDPluginProcess class documentation describes this class in detail.

NDPluginProcess.h defines the following parameters. It also implements all of the standard plugin parameters from NDPluginDriver. The EPICS database NDProcess.template provide access to these parameters, listed in the following table. Note that to reduce the width of this table the parameter index variable names have been split into 2 lines, but these are just a single name, for example `NDPluginProcessSaveBackground`.

Parameter Definitions in NDPluginProcess.h and EPICS Record Definitions in NDProcess.template
Parameter index variable | asyn interface | Access | Description | drvInfo string | EPICS record name | EPICS record type
--- | --- | --- | --- | --- | --- | ---
Background subtraction
NDPluginProcess SaveBackground | asynInt32 | r/w | Command to use the most recently acquired array as a background. Note that this recently acquired array should have been acquired with EnableBackground=0, or else that array will already have had the background subtracted, which is probably not what was intended! | SAVE_BACKGROUND | $(P)$(R)SaveBackground $(P)$(R)SaveBackground_RBV | bo bi
NDPluginProcess ValidBackground | asynInt32 | r/o | Flag indicating whether there is a valid background array that has been acquired for this array using SaveBackground. This flag will be Invalid (0) if no background has been acquired, or if the size of the array has changed since the background was last acquired. | VALID_BACKGROUND | $(P)$(R)ValidBackground_RBV | bi
NDPluginProcess EnableBackground | asynInt32 | r/w | Flag indicating whether the background array acquired with SaveBackground should be subtracted when processing the array. If ValidBackground=0 then no background subtraction is done even if EnableBackground=Enable. | ENABLE_BACKGROUND | $(P)$(R)EnableBackground $(P)$(R)EnableBackground_RBV | bo bi
Flat field normalization
NDPluginProcess SaveFlatField | asynInt32 | r/w | Command to use the most recently acquired array as a flat field. Note that this recently acquired array should have been acquired with EnableFlatField=0, or else that array will already have been flat field normalized, which is probably not what was intended! | SAVE_FLAT_FIELD | $(P)$(R)SaveFlatField $(P)$(R)SaveFlatField_RBV | bo bi
NDPluginProcess ValidFlatField | asynInt32 | r/o | Flag indicating whether there is a valid flat field array that has been acquired for this array using SaveFlatField. This flag will be Invalid (0) if no flat field has been acquired, or if the size of the array has changed since the flat field was last acquired. | VALID_FLAT_FIELD | $(P)$(R)ValidFlatField_RBV | bi
NDPluginProcess EnableFlatField | asynInt32 | r/w | Flag indicating whether the array should be divided by the flat field array (acquired with SaveFlatField) when processing the array. If ValidFlatField=0 then no flat field normalization is done even if EnableBackground=Enable. The processing step consists of: Array = Array / FlatField * ScaleFlatField | ENABLE_FLAT_FIELD | $(P)$(R)EnableFlatField $(P)$(R)EnableFlatField_RBV | bo bi
NDPluginProcess ScaleFlatField | asynFloat64 | r/w | The scale factor to multiply by after dividing the array by the flat field array. This scale factor is normally chosen so that the data after scaling fills the dynamic range of the output data type. | SCALE_FLAT_FIELD | $(P)$(R)ScaleFlatField $(P)$(R)ScaleFlatField_RBV | ao ai
Scaling and offset
NDPluginProcess EnableOffsetScale | asynInt32 | r/w | Flag indicating whether the array should be multiplied by Scale and then summed with Offset when processing the array. The processing step consists of: Array = Array * Scale + Offset | ENABLE_OFFSET_SCALE | $(P)$(R)EnableOffsetScale $(P)$(R)EnableOffsetScale_RBV | bo bi
NDPluginProcess AutoOffsetScale | asynInt32 | r/w | Processing this record will enable Offset and Scale calculations, and set the Offset=-min(Array) and Scale=MaxScale/(max(Array)-min(Array)), where MaxScale is the maximum value of the output data type. The output array will thus be scaled to completely fill the range of the output data type. Note that the calculation of the offset and scale factors is only done once when this record is processed, and these values are used for subsequent array callbacks, i.e. it does not autoscale on each array callback. Thanks to Tom Cobb for this addition. | AUTO_OFFSET_SCALE | $(P)$(R)AutoOffsetScale | busy
NDPluginProcess Scale | asynFloat64 | r/w | The scale factor to multiply by. | SCALE | $(P)$(R)Scale $(P)$(R)Scale_RBV | ao ai
NDPluginProcess Offset | asynFloat64 | r/w | The offset to add. | OFFSET | $(P)$(R)Offset $(P)$(R)Offset_RBV | ao ai
Low and high clipping
NDPluginProcess EnableLowClip | asynInt32 | r/w | Flag to control whether to clip values to the LowClip value for this array (0=Disable, 1=Enable). | ENABLE_LOW_CLIP | $(P)$(R)EnableLowClip $(P)$(R)EnableLowClip_RBV | bo bi
NDPluginProcess LowClip | asynFloat64 | r/w | The minimum allowed value for this array. If EnableLowClip=1, then all values in the array less than LowClip will be replaced by LowClip. | LOW_CLIP | $(P)$(R)LowClip $(P)$(R)LowClip_RBV | ao ai
NDPluginProcess EnableHighClip | asynInt32 | r/w | Flag to control whether to clip values to the HighClip value for this array (0=Disable, 1=Enable). | ENABLE_HIGH_CLIP | $(P)$(R)EnableHighClip $(P)$(R)EnableHighClip_RBV | bo bi
NDPluginProcess HighClip | asynFloat64 | r/w | The maximum allowed value for this array. If EnableHighClip=1, then all values in the array greater than HighClip will be replaced by HighClip. | HIGH_CLIP | $(P)$(R)HighClip $(P)$(R)HighClip_RBV | ao ai
NDPluginProcess DataType | asynInt32 | r/w | Data type of the output array (NDDataType_t). This can be different from the data type of the NDArray callback data. | PROCESS_DATA_TYPE | $(P)$(R)DataTypeOut $(P)$(R)DataTypeOut_RBV | mbbo mbbi
Recursive filter
NDPluginProcess EnableFilter | asynInt32 | r/w | Flag indicating whether the array should be processed with a recursive filter. The details of the filter operation are explained below. | ENABLE_FILTER | $(P)$(R)EnableFilter $(P)$(R)EnableFilter_RBV | bo bi
NDPluginProcess ResetFilter | asynInt32 | r/w | Command to reset the filter back to its initial state. | RESET_FILTER | $(P)$(R)ResetFilter $(P)$(R)ResetFilter_RBV | bo bi
NDPluginProcess AutoResetFilter | asynInt32 | r/w | If enabled then when NumFiltered=NumFilter the filter automatically resets. This can be very useful when using the Average or Sum filter modes. As soon as N sums or averages have been performed the filter resets, so the next sum or average is computed. | AUTO_RESET_FILTER | $(P)$(R)AutoResetFilter $(P)$(R)AutoResetFilter_RBV | bo bi
NDPluginProcess FilterCallbacks | asynInt32 | r/w | Choices are "Every array" and "Array N only". If "Every array" is selected then the plugin does callbacks for every incoming array it receives. If "Array N only" is selected then the plugin only does callbacks when NumFiltered=NumFilter. This can be very useful when using the Sum or Average filter modes. Callbacks are then done only when N sums or averages have been performed. If used with AutoResetFilter then as input arrays arrive the plugin will continually output one summed or averaged array after every N incoming arrays. | FILTER_CALLBACKS | $(P)$(R)FilterCallbacks $(P)$(R)FilterCallbacks_RBV | bo bi
NDPluginProcess NumFilter | asynInt32 | r/w | The characteristic number of arrays to use when filtering. The value of NumFiltered will increase as each array is processed, until it reaches the value of NumFilter, when it will no longer increase. The value of NumFiltered is used in the filter equations, as explained below. | NUM_FILTER | $(P)$(R)NumFilter $(P)$(R)NumFilter_RBV | longout longin
NDPluginProcess NumFiltered | asynInt32 | r/o | The number of arrays that have been processed by the filter since the filter was last reset. The value of NumFiltered is incremented as each array is processed, until it reaches the value of NumFilter, when it will cease incrementing. The value of NumFiltered is used in the filter equations, as explained below. | NUM_FILTERED | $(P)$(R)NumFiltered_RBV | longin
N.A. | N.A. | r/w | The filter type, chosen from a predefined list, as described below. | N.A. | $(P)$(R)FilterType | mbbo
NDPluginProcess OOffset | asynFloat64 | r/w | Output offset coefficient. | FILTER_OOFFSET | $(P)$(R)OOffset $(P)$(R)OOffset_RBV | ao ai
NDPluginProcess OScale | asynFloat64 | r/w | Output scale coefficient. | FILTER_OSCALE | $(P)$(R)OScale $(P)$(R)OScale_RBV | ao ai
NDPluginProcess OC1 | asynFloat64 | r/w | Output coefficient #1. | FILTER_OC1 | $(P)$(R)OC1 $(P)$(R)OC1_RBV | ao ai
NDPluginProcess OC2 | asynFloat64 | r/w | Output coefficient #2. | FILTER_OC2 | $(P)$(R)OC2 $(P)$(R)OC2_RBV | ao ai
NDPluginProcess OC3 | asynFloat64 | r/w | Output coefficient #3. | FILTER_OC3 | $(P)$(R)OC3 $(P)$(R)OC3_RBV | ao ai
NDPluginProcess OC4 | asynFloat64 | r/w | Output coefficient #4. | FILTER_OC4 | $(P)$(R)OC4 $(P)$(R)OC4_RBV | ao ai
NDPluginProcess FOffset | asynFloat64 | r/w | Filter offset coefficient. | FILTER_FOFFSET | $(P)$(R)FOffset $(P)$(R)FOffset_RBV | ao ai
NDPluginProcess FScale | asynFloat64 | r/w | Filter scale coefficient. | FILTER_FSCALE | $(P)$(R)FScale $(P)$(R)FScale_RBV | ao ai
NDPluginProcess FC1 | asynFloat64 | r/w | Filter coefficient #1. | FILTER_FC1 | $(P)$(R)FC1 $(P)$(R)FC1_RBV | ao ai
NDPluginProcess FC2 | asynFloat64 | r/w | Filter coefficient #2. | FILTER_FC2 | $(P)$(R)FC2 $(P)$(R)FC2_RBV | ao ai
NDPluginProcess FC3 | asynFloat64 | r/w | Filter coefficient #3. | FILTER_FC3 | $(P)$(R)FC3 $(P)$(R)FC3_RBV | ao ai
NDPluginProcess FC4 | asynFloat64 | r/w | Filter coefficient #4. | FILTER_FC4 | $(P)$(R)FC4 $(P)$(R)FC4_RBV | ao ai
NDPluginProcess ROffset | asynFloat64 | r/w | Reset offset coefficient. | FILTER_ROFFSET | $(P)$(R)ROffset $(P)$(R)ROffset_RBV | ao ai
NDPluginProcess RC1 | asynFloat64 | r/w | Filter coefficient #1. | FILTER_RC1 | $(P)$(R)RC1 $(P)$(R)RC1_RBV | ao ai
NDPluginProcess RC2 | asynFloat64 | r/w | Filter coefficient #2. | FILTER_RC2 | $(P)$(R)RC2 $(P)$(R)RC2_RBV | ao ai

### Recursive filter implementation

The recursive filter performs filtering in the time (not spatial) domain. It is implemented in a fairly general manner, so that a variety of filters can be implemented. These include integrating filters and differentiating filter types. The recursive filter stores one "filter" array internally. Using this internal filter array, and a new input array, it computes an output array, and a new version of the filter array. The equations governing the output array and new filter array are:

    O[n] = OOffset + OScale*((OC1 + OC2/N)*F[n-1] +
                             (OC3 + OC4/N)*I[n])
    F[n] = FOffset + FScale*((FC1 + FC2/N)*F[n-1] +
                             (FC3 + FC4/N)*I[n])
    On filter reset
    F[0] = ROffset + RC1*F[0] + RC2*I[0]

    where
      I[n] = New input array from callback
      I[0] = First input array after a filter reset
    F[n-1] = Stored filter array
      F[n] = New filter array
      F[0] = Current filter array when filter is reset.
             May be a copy of I[0] if there was no valid filter array.
         N = Current value of NumFiltered
      O[n] = Output array passed to clients

### Predefined filters

The NDProcess.template database implements the following predefined filters using the $(P)$(R)FilterType record. The implementation of these predefined filter types is done entirely in the database, not in the plugin code. The database simply downloads values of OC1-OC4, FC1-FC4, and RC1-RC2 that result in predefined filter behaviours. The operation of the recursive filter is by no means limited to these fixed filter types, they are simply provided as a convenience, and can be easily modified or extended. Note that the database does not download values of OOffset, OScale, FOffset, FScale, or ROffset, so these remain unchanged when a new filter is selected. The equations below do not include these offset and scale values, but they are useful in many circumstances.

The NDProcess_settings.req file for save/restore saves the values of all of the filter coefficients, and downloads them when EPICS initialized at iocInit. It also saves the FilterType, but does not process this record at iocInit, so that any customized filter coefficients will be preserved, and will not be replaced by the defaults for that filter type.

#### Recursive Average

The recursive average filter does input array averaging. It is defined as:

    O[n] = F[n] = (1-1/N)*F[n-1] + 1/N*I[n]

N is the characteristic number of arrays in the average. For example, if N=100, then each new array is weighted by 0.01 and the previous filter value is weighted by 0.99. This is an infinite impulse response (IIR) filter, because the effect of one array never complelely dissappears, but rather decays exponentially. When this filter is reset the filter array is initialized with the first input array. This filter can be used to decrease the noise and increase the dynamic range of a repetitive input signal with a small signal. In that case it can be useful to use set EnableOffsetScale=Enable and set Scale to a large enough number to more fully use the dynamic range of the output data type. The averaged signal will then better use the dynamic range of the output data type.

#### Average

The average filter does input array averaging. It is defined as:

    O[n] = F[n] = F[n-1] + 1/N*I[n]

N is the number of arrays in the average. For example, if N=100, then each new array is weighted by 0.01. When this filter is reset the filter array is initialized to 0. This filter typically cannot be run forever, because the output grows monotonically and will lead to overflow. However, if AutoResetFilter is enabled then the filter will be reset when NumFiltered=NumFilter. If ArrayCallbacks is "Array N only" then callbacks will be done only with the final averaged value.

#### Sum

The sum filter does input array summing. It is defined as:

    O[n] = F[n] = F[n-1] + I[n]

This filter simply computes the sum of input arrays. When this filter is reset the filter array is initialized to 0. It is often necessary to set the output data type to one with a larger maximum value than the input array to prevent overflow. If AutoResetFilter is enabled then the filter will be reset when NumFiltered=NumFilter. If ArrayCallbacks is "Array N only" then callbacks will be done only with the final summed value.

#### Difference

The difference filter computes the difference between frame N and frame N-1. It is defined as:

    O[n] = -F[n-1] + I[n]
    F[n] = I[n]

Note that the difference will often be a small signed number. If the output datatype is unsigned, or if a display client expects unsigned numbers, then it can be useful to set OOffset to a non-zero value to add an offset to the result. For example, if the output data type is UInt8 (unsigned 8-bit), then setting OOffset to 128 will produce an unsigned image centered at 128. Similarly OScale can be used to increase the range of the difference by multiplying by a factor greater than 1, although this will not increase the dynamic range of the result, only the absolute range.

#### Recursive Average Difference

This filter computes the difference between frame N and the recursive average of previous frames. It is a combination of the Difference and Recursive Average filters described above. The new filter is a recursive average, but the output is the difference between the current frame and that recursive average. It is defined as:

    O[n] = -F[n-1] + I[n]
    F[n] = (1-1/N)*F[n-1] + 1/N*I[n]

N is again the characteristic number of arrays in the average.

#### Copy to Filter

This filter simply copies the input array to the filter array and the output array. It is defined as:

    O[n] = F[n] = I[n]

This filter can be used to load the filter (F) with a certain array, which is then subsequently used for some other filter type.

## Configuration

The NDPluginProcess plugin is created with the NDProcessConfigure command, either from C/C++ or from the EPICS IOC shell.

    NDProcessConfigure(const char *portName, int queueSize, int blockingCallbacks,
                       const char *NDArrayPort, int NDArrayAddr,
                       int maxBuffers, size_t maxMemory,
                       int priority, int stackSize)

For details on the meaning of the parameters to this function refer to the detailed documentation on the NDProcessConfigure function in the NDPluginProcess.cpp documentation and in the documentation for the constructor for the NDPluginProcess class.

## Screen shots

The following is the MEDM screen that provides access to the parameters in NDPluginDriver.h and NDPluginProcess.h through records in NDPluginBase.template and NDProcess.template. In this example the input image is first offset by -4 and scaled by 35. That result is then clipped to a minimum of 0 and a maximum of 255. The image is then run through a recursive averaging filter with N=100.

### NDProcess.adl

Image collected with 30 microsecond exposure time, so it is very noisy. This is the image output from the NDPlugProcess plugin with Offset and Scale as shown above, but with the recursive filter disabled.

NDPluginStats plugin connected to the NDPluginProcess filter for the noisy image above. Note that there are only 6 non-zero intensities in the histogram, because the brightest pixels are only 6 A/D units above background.

Same image collected with 30 microsecond exposure time. This is the image output from the NDPlugProcess plugin with Offset and Scale as shown above, with the recursive filter enabled. Note the dramatic improvement in signal to noise.

NDPluginStats plugin connected to the NDPluginProcess filter for the filtered image above. Note that there are many non-zero intensities in the histogram, because the filtering is improving the dynamic range significantly.