Add Zope4 support, while keeping Zope2 support for now

See merge request nexedi/wendelin!110

Base_wendelinTextToNumpy just takes a string and pass it to numpy.load,
but numpy.load will load pickles (problem from https://github.com/numpy/numpy/issues/12759 ).
Therefore we disallow loading pickles here until this becomes the
default in newer numpy versions.
Co-authored-by: Jérome Perrin <jerome@nexedi.com>

self.publish() had a bug regarding status code for empty responses on
Zope2 and that bug was fixed in Zope4, this adjust the code to support
both versions.

this is already done in ERP5

See merge request !111

   All modules should use this base category.

With erp5@a254bf50 numpy
was allowed in erp5 upstream Restricted.py. There is no need for any
duplication in wendelin.

Tested at: https://erp5js.nexedi.net/#/test_result_module/20230223-AE5B8F75

See merge request nexedi/wendelin!109

erp5_wendelin_data_lake_ingestion_default_security_model: those module's Business Application are big data

See merge request nexedi/wendelin!106

Relicense to GPLv3+ with wide exception for all Free Software / Open Source projects + Business options.

See merge request nexedi/wendelin!105

Relicense to GPLv3+ with wide exception for all Free Software / Open Source projects + Business options.

Nexedi stack is licensed under Free Software licenses with various exceptions
that cover three business cases:

- Free Software
- Proprietary Software
- Rebranding

As long as one intends to develop Free Software based on Nexedi stack, no
license cost is involved. Developing proprietary software based on Nexedi stack
may require a proprietary exception license. Rebranding Nexedi stack is
prohibited unless rebranding license is acquired.

Through this licensing approach, Nexedi expects to encourage Free Software
development without restrictions and at the same time create a framework for
proprietary software to contribute to the long term sustainability of the
Nexedi stack.

Please see https://www.nexedi.com/licensing for details, rationale and options.

53b0c240 split categories to a separate bt5 which we need to install.

See merge request nexedi/wendelin!104

same as others

data mapping is used to map complex data into a single value

the idea is if an object has complet data to process, like (object, value1, value2, value3,value4....), we can mapping (object, value1, value2, value3,value4....) into a single value X, then we process only X to make it faster

here is an use case:

we have two 2D data arrays with 5 columns, the first column is the name of object, the other fours are the differents value of this object

Data ArrayA:

| object | value1 | value2 | value3 | value4 |
| ------ | ------ | ------ | ------ | ------ |
| X | 1 | 2 | 3 | 4 |
| Y | 5 |  6 | 7 | 8 |
| Z | 9 |  10 | 11 | 12 |

Data ArrayB:

| object | value1 | value2 | value3 | value4 |
| ------ | ------ | ------ | ------ | ------ |
| X | 1 | 2 | 3 | 4 |
| Y | 5 |  8 | 7 | 8 |
| Z | 9 |  10 | 192 | 12 |

now we need to compare data array A to data array B to find which object inside A has different value.

without data mapping, we need to compare each object's 4 values, the complexity is O(2^n)

with data mapping:

we map those values:

(X, 1, 2, 3, 4)  ==> 1

(Y, 5, 6, 7, 8)  ==> 2

(Z, 9, 10, 11,12) ==>3

(Y, 5, 8, 7, 8) ==> 4

(Z, 9, 10, 192, 12) ==> 5

Data ArrayA:

| object |
| ------ |
| 1 |
| 2 |
| 3 |

Data ArrayB:

| object |
| ------ |
| 1 |
| 4 |
| 5 |

then compare 1D array is fast, the complexity is O(n)

no idea why it's like this, let's just sort it to make furture commit easily

See merge request nexedi/wendelin!103

by moving categories except big_data to a delicated bt5, we can easily install erp5_wendelin without category conflict
big_data is used code to create and execute data analysis, maybe more.
erp5_wendelin bt5 can't work without this category