Explore the ASAM ODS Data Model#
In this example Notebook, we show you how to explore the data model of your Peak ODS Server.
Understanding the data model is important once it comes to query (instance) data from your Peak ODS Server.
The first sections are on initializing and connecting. The fun starts with “ASAM ODS Data Model”.
Dependencies for this notebook#
The ASAM ODSBox contains some functionality that wraps the ODS HTTP API making using Python easier ;-)
try:
import odsbox
except:
!pip install odsbox
from odsbox.con_i import ConI
ASAM ODS Data Model#
Establish session#
The ODS HTTP API is a session based API. The session ID is called conI in the ODS documentation. The ASAM ODSBox uses con_i as API object representing the session. Close this session to release the connection license. Otherwise the session will be auto closed after 30 minutes of inactivity.
conI = ConI(url='http://79.140.180.128:10032/api', auth=('Demo','mdm'))
Explore the Data Model#
The data model describes the way your data is stored in the Peak ODS Server. The data model consists of entities (you can compare them to class-definitions) and their according attributes and relations to other entities.
model = conI.model()
List all Entities#
Entities are the major building blocks of the data model. You typically query for instances of entities to explore your data. Let’s list all entities to see what we have
for entity in model.entities:
print(entity)
Unit
CatUnitUnderTestComp
TplUnitUnderTestComp
CatUnitUnderTestAttr
MDMGroup
TplTestStep
MeaResult
MDMLocalization
TplSensorAttr
TestSequence
TplSensor
Action
ValueList
InitToken
ExtSystem
TplTestEquipmentComp
Condition
TplMeaResult
ExternalComponent
TestEquipment
DynToken
Classification
CatTestSequenceAttr
CatSensorAttr
Environment
SystemParameter
ResultParameter
NameMap
ToCondition
TplParameter
Role
MDMRole
StructureLevel
LocalColumn
Test
TplTestStepUsage
WorkflowParameterSet
ProjectDomain
MDMLog
CatSensor
StatusValidity
CatTestEquipmentAttr
MDMTagParameter
ValueListValue
TplTestSequenceComp
ExtSystemAttr
TplSubMatrix
Project
Workflow
CatTestEquipmentComp
TplTestSequenceRoot
Domain
WorkflowRun
TestStep
FavouriteLists
TplTestEquipmentRoot
PhysDimension
TplGroup
UnitUnderTest
MDMTagParameterSet
SubMatrix
MDMFunction
TokenType
Status
User
TplUnitUnderTestAttr
MDMTag
CatTestSequenceComp
Quantity
TplTest
TplParameterSet
TplTestEquipmentAttr
MDMAttr
ResultParameterSet
TplUnitUnderTestRoot
ToAction
WorkflowParameter
TplTestSequenceAttr
UserParameter
AttributeMap
ActionRun
MeaQuantity
Examine a specific entitiy#
The entitiy definition contains some information of the entity itself and all attributes and references of this entity.
Have a specifc look to the base_name - in other data models your specific entity may have a diferent name. You can use the base_name for data model agnostic queries.
Also the aid can be of importance of you deal directly with the ASAM ODS REST API, not using JAQueL.
model.entities["Unit"]
name: "Unit"
base_name: "AoUnit"
aid: 54
attributes {
key: "dB"
value {
name: "dB"
data_type: DT_BOOLEAN
length: 1
id: 7
}
}
attributes {
key: "dB_reference_factor"
value {
name: "dB_reference_factor"
data_type: DT_FLOAT
length: 1
id: 8
}
}
attributes {
key: "Offset"
value {
name: "Offset"
base_name: "offset"
data_type: DT_DOUBLE
length: 1
obligatory: true
id: 6
}
}
attributes {
key: "Name"
value {
name: "Name"
base_name: "name"
data_type: DT_STRING
length: 50
obligatory: true
unique: true
id: 1
}
}
attributes {
key: "MimeType"
value {
name: "MimeType"
base_name: "mime_type"
data_type: DT_STRING
length: 256
obligatory: true
id: 2
}
}
attributes {
key: "Id"
value {
name: "Id"
base_name: "id"
data_type: DT_LONGLONG
length: 1
obligatory: true
unique: true
autogenerated: true
}
}
attributes {
key: "Factor"
value {
name: "Factor"
base_name: "factor"
data_type: DT_DOUBLE
length: 1
obligatory: true
id: 5
}
}
attributes {
key: "Description"
value {
name: "Description"
base_name: "description"
data_type: DT_STRING
length: 500
id: 3
}
}
attributes {
key: "DateCreated"
value {
name: "DateCreated"
base_name: "version_date"
data_type: DT_DATE
length: 30
id: 4
}
}
relations {
key: "WorkflowParameter"
value {
name: "WorkflowParameter"
base_name: "parameters"
inverse_name: "Unit"
inverse_base_name: "unit"
entity_name: "WorkflowParameter"
entity_aid: 73
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
relations {
key: "TplParameter"
value {
name: "TplParameter"
base_name: "parameters"
inverse_name: "Unit"
inverse_base_name: "unit"
entity_name: "TplParameter"
entity_aid: 71
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
relations {
key: "ResultParameter"
value {
name: "ResultParameter"
base_name: "parameters"
inverse_name: "Unit"
inverse_base_name: "unit"
entity_name: "ResultParameter"
entity_aid: 66
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
relations {
key: "Quantities"
value {
name: "Quantities"
base_name: "quantities"
inverse_name: "Unit"
inverse_base_name: "default_unit"
entity_name: "Quantity"
entity_aid: 55
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
relations {
key: "PhysDimension"
value {
name: "PhysDimension"
base_name: "phys_dimension"
inverse_name: "Units"
inverse_base_name: "units"
entity_name: "PhysDimension"
entity_aid: 47
range_min: 1
range_max: 1
inverse_range_max: -1
relation_type: RT_INFO
relationship: RS_INFO_TO
}
}
relations {
key: "MeaQuantities"
value {
name: "MeaQuantities"
base_name: "measurement_quantities"
inverse_name: "Unit"
inverse_base_name: "unit"
entity_name: "MeaQuantity"
entity_aid: 80
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
relations {
key: "MDMTagParameter"
value {
name: "MDMTagParameter"
base_name: "parameters"
inverse_name: "Unit"
inverse_base_name: "unit"
entity_name: "MDMTagParameter"
entity_aid: 65
virtual_reference: true
range_max: -1
inverse_range_max: 1
relation_type: RT_INFO
relationship: RS_INFO_FROM
}
}
Examine Attributes#
Looking at the attributes is also important when examine the data model. Like entities, also attributes can have a base_name to be used for data model agnostsic queries.
Furthermore data_type and obligatory information is important - especially when it comes to writing data
model.entities["Unit"].attributes["Name"]
name: "Name"
base_name: "name"
data_type: DT_STRING
length: 50
obligatory: true
unique: true
id: 1
Examine Relations#
Relations describe the connection between entities. In a data model relations are important for exploring the data neighbors. Relations have names to identify them (think of identifying a specific tire like “front left”) and the definition of the entity they’re pointing to: the entity_name. With range_min and range_max you can determine whether a relation is obligatory. In case a relation has also a (parallel) backward relation, the inverse_name contains this information.
model.entities["Unit"].relations["PhysDimension"]
name: "PhysDimension"
base_name: "phys_dimension"
inverse_name: "Units"
inverse_base_name: "units"
entity_name: "PhysDimension"
entity_aid: 47
range_min: 1
range_max: 1
inverse_range_max: -1
relation_type: RT_INFO
relationship: RS_INFO_TO
Close session#
Don’t forget to close the session to release the connection license. Otherwise the session will be auto closed after 30 minutes of inactivity.
conI.logout()
License#
Copyright © 2024 Peak Solution GmbH
The training material in this repository is licensed under a Creative Commons BY-NC-SA 4.0 license. See LICENSE file for more information.