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- Let P and Q be datasets loaded into DataScope.
- Then the following builtin functions can be
- applied:
- 1. Standard math functions (angles in radians):
- sin(Q) asin(Q) sinh(Q) log(Q) abs(Q) pow(Q,P)
- cos(Q) acos(Q) cosh(Q) log10(Q) sqrt(Q)
- tan(Q) atan(Q) tanh(Q) exp(Q) atan2(Q,P)
- 2. Standard trig functions (angles in degrees):
- dsin(Q) dasin(Q) dsinh(Q) datan2(Q,P)
- dcos(Q) dasin(Q) dcosh(Q)
- dtan(Q) datan(Q) dtanh(Q)
- 3. Misc. functions:
- transpose(Q) transpose the dataset
- dtor(Q) convert degrees to radians
- rtod(Q) convert radians to degrees
- shl(Q) shift data to the left
- shr(Q) shift data to the right
- shu(Q) shift data up
- shd(Q) shift data down
- mean(Q) mean of the data
- sdev(Q) standard deviation of the data
- min(Q) min of the data
- max(Q) max of the data
- pts(Q) number of data points
- cols(Q) number of columns
- rows(Q) number of rows
- rowrange(Q) range of row scale values
- colrange(Q) range of column scale values
- rowmean(Q) mean of distance between rows
- colmean(Q) mean of distance between columns
- rowsdev(Q) std. dev. of distance between rows
- colsdev(Q) std. dev. of dist. between columns
- 4. Discrete differences and convolutions:
- ddx(Q) difference from left to right
- ddy(Q) difference from top to bottom
- d2dx(Q) 2nd deriv. from left to right
- d2dy(Q) 2nd deriv. from top to bottom
- lap(Q) 5-point Laplacian
- lap5(Q) same as lap(Q)
- lap9(Q) 9-point Laplacian
- kernel(Q,P) convolution kernel operation, where
- P is a convolution kernel as defined in
- the manual. P may be 3x3, 5x5, or 7x7.
-