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 !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 !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 !101

This commit is a rebase of the following original commits:

Analysis execution methods supporting transient in- and output

---

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 !100

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

pass causality_relative_url to operation script to support multiple intervals with same sensor at same location