Difference between revisions of "Special Functions"
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* w is an [[IXTdataset_1d]] object | * w is an [[IXTdataset_1d]] object | ||
* ww is an [[IXTdataset_2d]] object | * ww is an [[IXTdataset_2d]] object | ||
+ | |||
+ | |||
+ | ==Noisify== | ||
+ | |||
+ | |||
+ | Noisify is a function used to add random noise to a dataset. It is good for testing fit functions etc. | ||
+ | |||
+ | |||
+ | <pre>>> w= noisify(w) | ||
+ | >> w = noisify(w, factor) | ||
+ | >> w = noisify(w,'poison')</pre> | ||
+ | |||
+ | |||
+ | * w may be an [[IXTdataset_1]] or [[IXTdataset_2d]] object | ||
+ | * Output will be the same as input | ||
+ | * Noise will have a gaussian distribution unless given the 'poison' command | ||
+ | * Standard deviation = factor * (maximum signal value) | ||
+ | * Default factor is 0.1 | ||
+ | * Mean value at a point in the poison distribution method is the y value. | ||
+ | will add random noise to the |
Revision as of 08:33, 1 April 2008
Some special functions exist that are useful when writing scripts that use Libisis
Contract Functions
These functions are used to contract an array of datasets into a single dataset.
>> wwout = contract(ww)
- ww is an array of IXTdataset_2d objects
- wwout is a single IXTdataset_2d object
- Two datasets must have matching x dimensions
- Each element of ww will be appended to the y dimension in the previous ww
For instance, if a dataset has ww(1).x = [1, 2, 3, 4] ww(1).y = [1, 2, 3, 4] ww(2).x = [1, 2, 3, 4] ww(2).y = [6, 7, 8] then wwout will be a dataset with wwout.x = [1, 2, 3, 4] and wwout.y = [1, 2, 3, 4, 6, 7, 8].
Expand Functions
These functions are used to expand a single IXTdataset_2d into an array of datasets.
>> wwout = expand_d2d(ww) >>wwout = expand_listd2d(ww, list)
- Input is an IXTdataset_2d object
- Output is an IXTdataset_2d object
- Output will be an array, each element containing just one y value
- If list is given, only the y indicies in list will be expanded
For instance, if a dataset ww has y values ww.y = [32, 44, 55, 66] and list was [2, 3] then wwout will be a 2 element array of IXTdataset_2d objects with wwout(1).y = 44 and wwout(2).y = 55
>> wout = expand_d1d(ww) >> wout = expand_listd1d(ww, list)
- Input is an IXTdataset_2d object
- Output is an IXTdataset_1d object
- Output will be an array, each element pertaining to one y row in the input
- If list is give, only the y indicies in list will be expanded
Conversion Functions
Conversion functions convert from one object type or data type to another
>> wwout = oned_to_twod(w)
- Input is an array of IXTdataset_1d objects
- Output is an IXTdataset_2d object or array of IXTdataset_2d objects
- Output will contain one y row for each element of the input
- y values will be [1, 2, 3 ...]
- x values in output will be the same as input
- Signal values in output will be the same as input
- If possible, the output dataset will be contracted to a single IXTdataset_2d object
>> wout = hist2point(w) >> wwout = hist2point_x(ww) >> wwout = hist2point_y(ww)
- Converts one dimension (x or y) from histogram data to point data
Functions to Aid Plotting
Standard functions exist to help with plotting
>>[xlab, ylab] = make_label(w) >> [xlab, ylab, zlab] = make_label(ww)
- xlab is the label for the x axis
- ylab is the label for the y axis
- zlab is the label for the z axis
- w is an IXTdataset_1d object
- ww is an IXTdataset_2d object
Noisify
Noisify is a function used to add random noise to a dataset. It is good for testing fit functions etc.
>> w= noisify(w) >> w = noisify(w, factor) >> w = noisify(w,'poison')
- w may be an IXTdataset_1 or IXTdataset_2d object
- Output will be the same as input
- Noise will have a gaussian distribution unless given the 'poison' command
- Standard deviation = factor * (maximum signal value)
- Default factor is 0.1
- Mean value at a point in the poison distribution method is the y value.
will add random noise to the