Editing Dataframe package

Dataframe, Data manipulation toolbox similar to R data.frame 

At an mature development stage. [http://hg.code.sf.net/p/octave/dataframe hg]
*Maintainer: Pascal Dupuis 
*Contributors: 

*Package: [http://octave.sourceforge.net/dataframe/ dataframe]

This package permits to handle complex (both in the sense of complex numbers and high complexity) data as if they were ordinary arrays, except that each column MAY possess a different type. It also provides a fairly complete interface to CSV files, permitting to cope with a number of oddities, like e.g., CSV files starting with a header spread over a few lines. The resulting array tries as far as it can to mimic an array in such a way that binary operators and usual functions will work as expected.

Meta-information is also handled. Rows and columns may have a name, and this name is searchable. If for whatever reason the ordering of a CSV file changes, searching by column names will return the expected information.

To get a first taste, let's load the test csv file coming with the package:
  >> experiment = dataframe('data_test.csv')
  warning: load: '/home/padupuis/matlab/dataframe/inst/data_test.csv' found by searching load path
  warning: fopen: '/home/padupuis/matlab/dataframe/inst/data_test.csv' found by searching load path
  ans = dataframe with 10 rows and 7 columns
  Src: data_test.csv
  Comment: #notice there is a extra separator
  Comment: # a comment line and an empty one
  Comment: # the next lines use \r\n \r and \f as linefeed
  Comment: # one empty input field
  _1  DataName   VBIAS   Freq   x_IBIAS_          C       GOUT  OK_
  Nr      char  double double     double     double     double char
   1 DataValue -6.0000 300000 1.6272e-11 7.0215e-13 1.6044e-07    A
   2 DataValue -5.8000 300000 1.5990e-11 6.9607e-13 1.5728e-07    E
   3 DataValue -5.6000 300000 1.3790e-11 6.9048e-13 1.5489e-07    !
   4 DataValue -5.4000 300000 1.4420e-11 6.8517e-13 1.5478e-07    ?
   5 DataValue -5.2000 300000 1.2930e-11 6.7965e-13 1.5189e-07    C
   6 DataValue -5.0000 300000 1.2610e-11 6.7444e-13 1.4931e-07    B
   7 DataValue -4.8000 300000 1.4390e-11 6.7011e-13 1.4876e-07    A
   8 DataValue -4.6000 300000 1.0890e-11 6.6416e-13 1.4890e-07    3
   9 DataValue -4.4000 300000         NA 6.5859e-13 1.4558e-07    C
  10 DataValue -4.2000 300000 1.0610e-11 6.5355e-13 1.4431e-07    B

Those data were produced while performing a voltage sweep on a sensor, measuring with an impedance bridge
the parallel capacitor and conductance at a given frequency.

The first lines contain few meta-information: name of the source file and a few comments found in the 
csv file. The purpose is to annotate the results.

Then we have the content. Each column starts with a name, then a type. Next we find the content lines, each 
of them with an index. Then we find the content; control values (polarization voltage, applied frequency), 
then measured values: DC current, capacitor, conductance. The last column is categorical: the user introduced
some code telling if the result makes senses or not.

Let us now select the control values:
  cv = experiment(1:3, ["Vbias"; "Freq"])
  cv = dataframe with 3 rows and 1 columns
  Src: data_test.csv
  Comment: #notice there is a extra separator
  Comment: # a comment line and an empty one
  Comment: # the next lines use \r\n \r and \f as linefeed
  Comment: # one empty input field
  _1   Freq
  Nr double
   1 300000
   2 300000
   3 300000

The selection occurred on a range for the lines, by names on the column. The search criteria is here a 
string array. All columns whose name match are returned.

The result is returned as a dataframe. This can be changed:
 >> experiment.array(6, "OK_")
 ans = B
 >> class(ans)
 ans = char

When selecting vectors, this transformation in array is automatic. The DC current is contained in elements
31 to 40 (fourth column): 
  >> experiment(31:40)
 ans =
 Columns 1 through 9:
   1.6272e-11   1.5990e-11   1.3790e-11   1.4420e-11   1.2930e-11   1.2610e-11   1.4390e-11   1.0890e-11           NA
 Column 10:
   1.0610e-11
Note that the access 'experiment("x_IBIAS")' is illegal: does it refer to row or column names ?

;Accessing in this pseudo-structure way is valid in the following cases:
;choosing the output format: array, cell, dataframe (may be abbreviated as 'df')
;attribute selection: rownames, colnames, rowcnt, colcnt, rowidx, types, source, header, comment
;constructor call: new (no other deferencing may occur
;column selection: just provide one valid column name
To be similar to R implementation, constructs such as x.as.array are also allowed.

A simple example:
 truc={"Id", "Name", "Type";1, "onestring", "bla"; 2, "somestring", "foobar";}
 truc =
 {
   [1,1] = Id
   [2,1] =  1
   [3,1] =  2
   [1,2] = Name
   [2,2] = onestring
   [3,2] = somestring
   [1,3] = Type
   [2,3] = bla
   [3,3] = foobar
 }
 >> tt=dataframe(truc)
 tt = dataframe with 2 rows and 3 columns
 _1     Id       Name   Type
 Nr double       char   char
  1      1  onestring    bla
  2      2 somestring foobar

The first cell line is intended to contain column names; the rest is column content. The type is automatically inferred from the cell content. Now let us select one column by its name:
 >> tt(:, 'Name')
 ans = dataframe with 2 rows and 1 columns
 _1       Name
 Nr       char
 1  onestring
 2 somestring

In this case, a sub-dataframe is returned. Struct-like indexing is also implemented:
 >> tt.Id
 ans =
   1
   2
When the output is a vector and can be simplified to something simple ... it is.

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