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

See merge request nexedi/wendelin!102

Necessary for listbox.

This fixes the following error:

  >>> data_bucket_stream = context.data_stream_module.newContent(
  >>>   portal_type = "Data Bucket Stream"
  >>> )
  >>> data_bucket_stream.insertBucket(1, 'testBucket')
  >>> data_bucket_stream.Base_getConsistencyList()

  # Error Type: AttributeError
  # Error Value: 'NoneType' object has no attribute 'getPortalType'

The returned empty lists are valid return values as it can be seen in the Folder class methods:

https://lab.nexedi.com/nexedi/erp5/blob/a17bb910/product/ERP5Type/Core/Folder.py#L1530
https://lab.nexedi.com/nexedi/erp5/blob/a17bb910/product/ERP5Type/Core/Folder.py#L1026

(in restricted python)

This didn't work anymore in restricted python.

(Drop deprecated DCWorkflows)

See merge request nexedi/wendelin!101

This commit is a rebase of the following original commits:

Analysis execution methods supporting transient in- and output

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Conflicting files:
bt5/erp5_wendelin/SkinTemplateItem/portal_skins/erp5_wendelin/DataAnalysis_executeDataOperation.py
bt5/erp5_wendelin/SkinTemplateItem/portal_skins/erp5_wendelin/DataAnalysis_executeDataOperation.xml
bt5/erp5_wendelin/SkinTemplateItem/portal_skins/erp5_wendelin/ERP5Site_executeDataAnalysisList.py

processing of analysis with transient items

---

Conflicts:
bt5/erp5_wendelin/SkinTemplateItem/portal_skins/erp5_wendelin/DataAnalysis_executeDataOperation.py
bt5/erp5_wendelin/SkinTemplateItem/portal_skins/erp5_wendelin/ERP5Site_createDataAnalysisList.py

See merge request nexedi/wendelin!100

The new tests ensures the correct creation of Data Analysis documents.