pi | the constant PI |
e | the constant e |
rand | generates a random floating point number between 0-1 each |
x | predefined range variable |
y | predefined range variable |
z | predefined range variable |
t | predefined range variable |
ij(image,x,y,z,t) | Access a value from an open ImageJ volume, all indices are zero based, use z & t = 0 for 2D or 3D images |
makearray(expression, index_operand, start value, stop value, increment) | Creates an array by evaluating expression repeatedly, changing the value of index_operand, beginning with start_value, incrementing by increment, until stop_value is reached |
Sumarray (array) | Sums the elements of array |
ttest(array1, array2) | Perform a ttest on the elements in the two arrays, and |
average(array) | returns the average of the elements in the array |
var(array) | returns the variance of the elements in the array |
concat (array1, array2, ..) | concatenate the arrays into a single array |
median(array) | returns the median value from the array |
normalize(array1, start, stop) | convenience function. returns a new array that is normalized by dividing all elements by the average of the elements between and including the elements at start and stop and subtracting 1 |
getallreps(image, x,y,z) | returns an array containing all of the reps for the given voxel |
anova(array1, array2,...) | NOT IMPLEMENTED - being fixed |
lregress(xdata, ydata) | perform a linear regression of the xy data. returns [pearson r, slope (b), meanx, meany] |
nlregress(expression, ydata, operand_to_modify,+ bounds, max_iterations, num_restarts) | perform a simplex regression. Returns array of optimal operand values, and the p value based on the regression using the Chi–square distribution. Expression is the generator of data to fit the data in the array ydata, operands to modify follow, an array (bounds) of 2*number of operands to modify elements specifies the bounds of the values on the operands to modify, and max_iterations and num_restarts can be user specified as well. |
avearrays(array1, array2, ...) | convenience function which creates a new array where each element is the average of the corresponding element from each of the arrays passed in. All input arrays must be the same length. |
addarrays(array1, array2, ...) | convenience function which creates a new array where each element is the sum of the elements at that index from the input arrays. All input arrays must be the same length. |
subarray(array1, start, stop) | convenience function which creates a new array which is the subset of elements between and including start and stop indices from the supplied array. Indexes are, as usual, zero based |
stdev(array) | Returns the standard deviation of the data in the supplied array |
covar(array1, array2) | Returns the covariance of the arrays |
corr(array1, array2) | Returns the correlation coef. of the arrays |
+ | addition |
- | subtraction, unary subtraction |
* | multiplication (NOTE implicit multiplication is not supported use 3*a, not 3a) |
/ | division |
^ | exponent |
ln(x) | natural logarithm |
sin(x) | sine (input in radians) |
cos(x) | cosine (input in radians) |
tan(x) | tangent (input in radians) |
acos(x) | inverse cosine |
asin(x) | inverse sine |
atan(x) | inverse tangent |
| x | | absolute value |
min(x,y) | minimum of x and y |
max(x,y) | maximum of x and y |
ceil(x) | nearest integer larger than x |
floor(x) | nearest integer smaller than x |
round(x) | nearest integer to x |
sqrt(x) | square root of x |
tdist(t,df) | returns the pvalue associated with the t distribution with tscore t, and degrees of freedom df (two tails, homoscedastic) as evaluated from the incomplete beta function: ![]() |
gammp(a,x) | the incomplete gamma function P(a,x)![]() |
gammq(a,x) | the incomplete gamma function complement Q(a,x) = 1- P(a,x) |
betai(a,b,x) | the incomplete beta function![]() ![]() |