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DataScope2.0.3.sit.hqx
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HTXT_11_.....Builtin Notebook Functions_
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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.