Change to readme

Author: peterhinch

README.md

@@ -75,6 +75,6 @@ Data arriving from transducers often needs to be filtered to render it useful. R
 
 FIR (finite impulse response) filters can be viewed as an extension of the moving average concept where each sample is allocated a different weight depending on its age. These weights are defined by a set of coefficients. The result is calculated by multiplying each sample by its coefficient before adding them; a moving average corresponds to the situation where all coefficients are set to 1. By adjusting the coefficients you can alter the relative weights of the data values, with the most recent having a different weight to the next most recent, and so on.
 
-In practice FIR filters can be designed to produce a range of filter types: low pass, high pass, bandpass, band stop and so on. They can also be tailored to produce a specific response to sudden changes (impulse response). The process of computing the coefficients is complex, but the link above provides a simple GUI approach. Set the application to produce 16 bit integer values, set your desired characteristics and press "Design Filter". Then press the Source Code tab. The filter tap values can then be cut and pasted into your Python code.
+In practice FIR filters can be designed to produce a range of filter types: low pass, high pass, bandpass, band stop and so on. They can also be tailored to produce a specific response to sudden changes (impulse response). The process of computing the coefficients is complex, but the link above provides a simple GUI approach. Set the application to produce 16 or 32 bit integer values, set your desired characteristics and press "Design Filter". Then proceed as suggested above to convert the results to Python code.