3. dataList

dataList contain a list of dataArray.

  • List of dataArrays allowing variable sizes and attributes.

  • Basic list routines as read/save, appending, selection, filter, sort, prune, interpolate, spline…

  • Multidimensional least square fit that uses the attributes of the dataArray elements.

  • Read/Write in human readable ASCII text of multiple files in one run (gzip possible) or pickle.

  • A file may contain several datasets and several files can be read.

  • For programmers: Subclass of list

For Beginners:

  • Create a dataList and use the methods from this object in point notations.:

    data=js.dL('filename.dat').
    data.prune(number=100)
    data.attr
    data.save('newfilename.dat')
    
  • The dataList methods should not be used directly from this module.

See dataList for details.

Example:

p=js.grace()
dlist2=js.dL()
x=np.r_[0:10:0.5]
D,A,q=0.45,0.99,1.2
for q in np.r_[0.1:2:0.2]:
   dlist2.append(js.dA(np.vstack([x,np.exp(-q**2*D*x),np.random.rand(len(x))*0.05])) )
   dlist2[-1].q=q
p.clear()
p.plot(dlist2,legend='Q=$q')
p.legend()
dlist2.save('test.dat.gz')

The dataarray module can be run standalone in a new project.

3.1. dataList Class

  • dataList creating by dataL=js.dL(‘filename.dat’) or from numpy arrays.

  • List columns can be accessed as automatic generated attributes like .X,.Y,.eY (see protectedNames). or by indexing as *dataL[:,0] -> .X * for all list elements.

  • Corresponding column indices are set by setColumnIndex() (default X,Y,eY = 0,1,2).

  • Multidimensional fitting of 1D,2D,3D (.X,.Z,.W) data including additional attributes. .Y (scalar) are used as function values at coordinates.

  • Attributes can be set like: dataL.aName= 1.2345 or dataL[2].aName= 1.2345

  • Individual elements and dataArray methods can be accessed by indexing data[2].bName

  • Methods are used as dataL.methodname(arguments)

3.2. Attribute Methods

3.3. Fit Methods

Least square fit

Prediction

3.4. Housekeeping Methods